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Eco-efficiency of grain production in China based on water footprints: A stochastic frontier approach
Accepted Manuscript Eco-efficiency of grain production in China based on water footprints: A stochastic frontier approach Jianfeng Song, Xiaonan Chen PII:
S0959-6526(19)32535-1
DOI:
https://doi.org/10.1016/j.jclepro.2019.117685
Article Number: 117685 Reference:
JCLP 117685
To appear in:
Journal of Cleaner Production
Received Date: 28 December 2018 Revised Date:
10 July 2019
Accepted Date: 15 July 2019
Please cite this article as: Song J, Chen X, Eco-efficiency of grain production in China based on water footprints: A stochastic frontier approach, Journal of Cleaner Production (2019), doi: https:// doi.org/10.1016/j.jclepro.2019.117685. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
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Eco-efficiency of grain production in China based on water footprints: a stochastic frontier approach
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Jianfeng Songa , Xiaonan Chena,∗
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College of Economics and Management, Northwest A&F University, Yangling, Shaanxi 712100, PR China
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Abstract
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Eco-efficiency has consistently been of interest to researchers and policy makers. Many methods
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have been employed to calculate eco-efficiency, with the exception of the stochastic frontier ap-
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proach, which is popular in efficiency and productivity analysis. Considering the strengths of the
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stochastic frontier approach and the features of grain production, an integrated WF-SFA method
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combining water footprint assessment and the stochastic frontier approach is proposed in this
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work. In the method, the green, blue and grey water footprints of grain production are calculated.
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Then, a translog stochastic frontier production function with actual grain output value as the only
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output and capital, labour and water footprints as the inputs is established. Next, eco-efficiency,
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which is defined as the ratio of the actual output to the potential output, can be assessed. This
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method is developed here to analyse the eco-efficiency of grain production and its determinants in
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China. The main empirical results are as follows. 1) The annual average grain production water
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footprint in China from 1997 to 2015 was 820.37 billion m3 . 2) The eco-efficiencies were esti-
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mated to be within the range of 0.424-0.986, with an average value of 0.807. There is potential
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for China to increase the environmental and ecological sustainability with its grain production
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system. 3) The per capita GDP, per capita water supply, proportion of government expenditure
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on environmental protection and proportion of non-disaster areas positively influenced the grain
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production eco-efficiency. In addition, the calculated output elasticities of the blue and grey water
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footprints of recent years were negative. These findings can help China design relevant policies
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of agricultural sustainability focused on crop distribution, efficient irrigation water use and nutri-
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ent and pollutant management. This research provides a basic framework for the eco-efficiency
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evaluation of grain production with the stochastic frontier approach which can inform policy and
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strategic development.
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Keywords:
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eco-efficiency, grain production, water footprint, stochastic frontier approach
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1. Introduction
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Grain production requires large amounts of resources (water, land, energy, and chemicals) and
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contributes to environmental pollution, especially water and soil pollution, due to the application of
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fertilizer, pesticides and insecticides and the consequent losses from the system (Thanawong et al.,
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2014). Increasing the production of major food crops to keep pace with projected increases in food
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demand while also saving national resources and protecting the environment is an international
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challenge, given the concerns over growing populations (Carberry et al., 2013). For policymaking,
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it is necessary to have indicators in this context, that is, indicators of economic and environmental
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efficiency, that compare the evolution of countries or sectors, set goals and implement effective
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policies, either globally or locally (Robaina-Alves et al., 2015).
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Economic efficiency reflects the ability of a production unit to obtain maximal economic output
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from a given set of production factor inputs (labour, capital, etc.) and the production technology.
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However, it does not imply resource and environmental efficiency (Yang and Zhang, 2018). To
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evaluate whether producers are making efficient use of resources and minimizing environmental
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impacts while achieving their economic objectives, economic-ecological efficiency, known as eco-
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efficiency, may be a useful operational concept (Thanawong et al., 2014).
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Eco-efficiency was first proposed as an instrument for sustainability analysis by Schaltegger
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and Sturm (1990) , and it was subsequently popularized by the World Business Council for
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Sustainable Development (WBCSD, 1992). According to the Organization for Economic Co-
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operation and Development (OECD, 1998), eco-efficiency is defined as “the efficiency with which
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ecological resources are used to meet human needs”, and it is calculated as the “product or service
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Corresponding author. Email-address: [email protected]. Telephone number: +86-29-87081140. Fax number: +86-29-87081140. Email addresses: [email protected] (Jianfeng Song), [email protected] (Xiaonan Chen) ∗
Preprint submitted to Journal of Cleaner Production
July 17, 2019
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value divided by environmental influence”. This definition has been adopted widely for illustrat-
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ing and estimating eco-efficiency. Although eco-efficiency has been further shaped and developed
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by many other studies, eco-efficiency generally reflects the ability to produce more goods and ser-
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vices while consuming fewer natural resources and producing less of an impact on the environment
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(Robaina-Alves et al., 2015).
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Eco-efficiency can be applied to different sectors, such as industrial processes, businesses or
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even to a specific product, and it can also be applied at a regional or global level (Caiado et al.,
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2017). Previous literature has presented two main general approaches to measure eco-efficiency.
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One is the ratio method as proposed by the OECD (1998), and the other is a frontier-based ap-
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proach, data envelopment analysis (DEA). Both approaches address the relative performance com-
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parison. A more detailed literature about eco-efficiency estimation is presented in Section 2.
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In 1996, the Badische Anilin und Soda Fabrik Corporation (BASF) developed the eco-efficiency ratio methodology which assesses both the economic and environmental impacts of chemicals,
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processes and products in their lifecycle (Landsiedel and Saling, 2002). After that, calculating
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the ratio of economic performance to environmental performance based on life cycle assessment
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has long been identified as the standard method of eco-efficiency analysis. The most common
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indicator of economic performance is profit, defined as the differences between revenues and
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costs (Hoang and Alauddin, 2012). Environmental impacts for eco-efficiency calculations usu-
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ally include energy use, resource use, water use, greenhouse gas emissions and ozone-depleting
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emissions (M¨uller et al., 2015). The ratio method is very straightforward and communicative, but
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when more than one economic or environmental indicator is concerned, aggregation into a single
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numerator or denominator requires appropriate methods and assumptions on aggregation weights
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(Coelli et al., 2005).
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Farrell (1957) expanded the measurement of efficiency and productivity to production with
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multiple inputs and/or outputs based on production frontiers. The basis for this measure is the
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radial contraction/expansion connecting inefficient observed points with (unobserved) reference
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points on the production frontier. If a decision-making units actual production point lies on the
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frontier, it is perfectly efficient. If the production point lies below the frontier, it is inefficient,
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and the ratio of the actual to potential production defines the level of the efficiency of the indi3
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vidual decision-making unit (Coelli et al., 2005). The estimation of efficiency can be categorized
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according to the assumptions and techniques used to construct the efficient frontier. On the one
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hand, parametric methods, such as DEA, estimate the frontier with statistical methods. On the
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other hand, nonparametric methods, such as the stochastic frontier approach (SFA), rely on linear
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programming to calculate piecewise linear segments of the efficient frontier (Coelli et al., 2005).
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DEA uses linear programming to construct a nonparametric piecewise linear production fron-
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tier using different return to scales and the possibility of multiple inputs and multiple outputs
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(Robaina-Alves et al., 2015).Kuosmanen and Kortelainen (2005) first introduced the DEA tech-
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nique to eco-efficiency analyses. Subsequently, many researchers have developed various DEA
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models to measure eco-efficiency by incorporating resource inputs and environmental output into
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the traditional input-output framework of productivity analysis (Yang and Zhang, 2018). DEA has
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an advantage because it is able to manage complex production environments with multiple inputs
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and outputs. The advantage over traditional single input single output measures becomes apparent
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when more than one environmentally detrimental input is involved (Reinhard et al., 2000). To date,
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DEA has been the most popular method used to measure eco-efficiency from a more aggregated
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perspective.
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Despite its strengths, DEA does not consider statistical noise, and all deviations from the pro-
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duction frontier are estimated as technical inefficiency, rendering DEA very sensitive to outliers
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(Robaina-Alves et al., 2015). As a deterministic method, DEA suffers from measurement errors
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in the included variables and the omission of unobserved and potentially relevant variables (Kon-
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todimopoulos et al., 2011). Not controlling for external influences may lead to erroneous DEA
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efficiency measurements, which, in turn, may provoke uninformed policymaking decisions (Kon-
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todimopoulos et al., 2011). A parametric stochastic frontier approach developed independently
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by Aigner et al. (1977) and Meeusen and van Den Broeck (1977) can overcome this shortcoming
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and has been applied to benchmark efficiency in many fields (Zhou et al., 2012). SFA assumes
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that the distance of a production unit from the best practice frontier is the sum of the following
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two components: “true” inefficiency and random fluctuations (Kontodimopoulos et al., 2011). In
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addition, the other advantages of SFA include the possibility of specification in the case of panel
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data, formal statistical testing of hypotheses and the construction of confidence intervals Reinhard
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et al. (2000). Despite its strengths, few studies analyse and evaluate eco-efficiency using SFA. The
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possible reasons are that the functional form used in SFA needs to be correctly specified and that
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SFA can only be used to estimate the efficiency of production with only one output as noted by
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Robaina-Alves et al. (2015).
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In the choice between DEA and SFA, a key question is whether one wants flexibility in the
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mean structure or precision in the noise separation (Bogetoft and Otto, 2011). Coelli (1995) con-
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cludes that the SFA is recommended for use in agricultural applications, which is the intention of
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this paper, because measurement error, missing variables, weather, etc. are likely to play a sig-
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nificant role in agriculture. Considering the advantages and restrictions of the SFA technique, an
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integrated WF-SFA methodology that combines a water footprint (WF) analysis within the SFA
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framework is proposed for the estimation of the eco-efficiency of Chinese grain production in this
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paper. In the first phase, a WF analysis is employed to quantify the resource consumption and en-
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vironmental impacts associated with grain production, and in the second phase, an SFA model in
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which the grain output value represents the only output and capital, labour and water footprints are
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chosen to represent the inputs is adopted to benchmark the relative performance of eco-efficiency.
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The WF is an indicator of the direct and indirect appropriation of freshwater resources with pro-
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duction or consumption (Hoekstra et al., 2011). The total WF includes green, blue and grey WFs.
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The green WF refers to the consumption of rainwater, the blue WF refers to the consumption of
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surface and groundwater, and the grey WF refers to pollution and is defined as the volume of
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freshwater required to assimilate a load of pollutants given the natural background concentrations
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and existing ambient water quality standards (Mekonnen and Hoekstra, 2011). A few studies have
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applied WF to eco-efficiency analyses without using the SFA technique. Egilmez and Park (2014)
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quantified the transportation-related carbon, energy and WF of a nation’s manufacturing sectors
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and evaluated the environmental and economic performance based on eco-efficiency scores. In
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contrast to previous studies, the three WFs representing resource consumption and environmental
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impact are considered inputs of the production function in this study. This study not only includes
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most externalities in grain production but also offers possibilities to measure eco-efficiency with
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the SFA technique. In the WF-SFA model, similar to studies using output-oriented DEA, eco-
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efficiency is defined as the ratio of the actual output to the frontier output (technical efficiency of
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ecological production). A more detailed description of the method is provided in Section 3. Compared to previous methods used for eco-efficiency estimation, the WF-SFA method has
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the following advantages. (1) The capital, labour and water footprints are selected to represent
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the inputs of agricultural production. Therefore, the estimated eco-efficiency indicator provides
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the overall outcome of the economic, resource and environmental efficiency of the joint use of
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all production factors. (2) Using this method, the production frontier is considered by technical
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inefficiency, measurement error, statistical noise and other non-systematic influences, avoiding
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the possibility that a large amount of random noise is potentially mistaken for inefficiency as in
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DEA. (3) This method can also explain the variations in the eco-inefficiency effects in terms of
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other variables in a single-stage approach. A determinant analysis of eco-efficiency can also be
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performed with the DEA technique but in a two-stage process. (4) The output elasticity of each
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factor (capital, labour and water footprints) can be calculated using the estimated parameters of
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the frontier of production to reveal the factor responsible for the good or bad performance of the
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region in terms of eco-efficiency.
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In this paper, we aimed to combine WF and SFA to assess the eco-efficiency of grain pro-
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duction in China. This aim is approached from three perspectives. (1) An integrated WF and
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SFA methodology is proposed to estimate eco-efficiency. (2) With this method, the regional eco-
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efficiency of grain production is estimated to illustrate the combination of food security and envi-
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ronmental sustainability in China. (3) Furthermore, the determinants of eco-efficiency are studied
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as well to address the key factors underlying eco-efficiency of grain production in China. In addi-
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tion, the output elasticities of inputs and the contribution of changes of eco-efficiency to the total
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factor productivity for grain production in China are discussed. The contribution of this study is
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twofold, namely, methodological and policy-oriented. On one hand, the WF-SFA method used in
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this study is a useful attempt in method extend for measuring eco-efficiency and can be used in
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other fields directly or after adjustment. On the other hand, the results of the empirical analysis
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can provide detailed information about resource use and technical impact in grain production and
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can be used to derive policy implications for agricultural sustainability in China.
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The rest of this paper is organized as follows. The next section briefly reviews the main meth-
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ods used for calculating eco-efficiency. Section 3 introduces the methodologies, including the 6
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method for assessing the WF and a grain production SFA model by introducing green, blue and
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grey WF inputs for estimating eco-efficiency. The regional panel data used in the empirical study
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is presented as well. Section 4 and Section 5 present the main results and discussions. Section 6
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summarizes the conclusions and implications.
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2. Literature review of eco-efficiency estimation
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There are two main general approaches to measuring eco-efficiency. The first involves the
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development of a ratio indicator of the economic performance per unit of environmental influence,
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and the second is based on the production frontiers to derive efficiency measures. Both approaches
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address the relative performance comparison (Hoang and Alauddin, 2012).
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2.1. The ratio method
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Eco-efficiency is commonly defined and measured as the ratio of the economic performance to
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the environmental influence (OECD, 1998). Eco-efficiency improves when negative environmental
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impacts decrease while the value of production is maintained or increased (G´omez-Lim´on et al.,
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2012). The higher the eco-efficiency indicator value, the higher the product or service value per
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unit of environmental burden (M¨uller et al., 2015).
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Assessing eco-efficiency requires indicators of both economic and environmental performances
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(Thanawong et al., 2014). The approach to situating proper economic performance and environ-
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mental influence indicators has developed and changed in accordance with the considered per-
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spective and the field of study. In 1996, BASF developed an eco-efficiency analysis approach
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to assess both the economic and environmental impacts for comparing different alternatives of a
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defined customer benefit over the whole life cycle (Sailing et al., 2002). It is based on life cycle
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assessment, and it provides a helpful tool in different fields of the evaluation of product or process
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alternatives (Sailing et al., 2002). Landsiedel and Saling (2002) extended this approach by con-
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sidering the assessment of toxicological risks. For better specifying the details of eco-efficiency,
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Huppes and Ishikawa (2005) established a framework for quantifying the eco-efficiency analysis
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with a ratio method.
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The choice of indicators for economic and environmental performance is an empirical ques-
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tion. There is little agreement on it. Generally, economic performance in an eco-efficiency analysis
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can be quantified in monetary units as sales or as “value added”, which is sales minus the costs of
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goods and services (Hoang and Alauddin, 2012). Cost-benefit analysis, based on life cycle costs,
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are commonly used to measure economic performance (Huppes and Ishikawa, 2005). In addition,
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the most common indicators of environmental performance are material consumption, energy con-
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sumption, emissions, toxicity potential and risk potential. These indicators were first used in the
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BASF methodology and are still used currently (Caiado et al., 2017). Life cycle assessment is
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well known, and it is the best methodology for assessing the potential environmental impact asso-
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ciated with a production, process, transport or other activity chain by evaluating the emission and
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resource consumption (Egilmez and Park, 2014).
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The ratio method has been widely used to calculate eco-efficiency in various fields, such as by
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product (Aoe, 2007; Park et al., 2007), company (Charmondusit and Keartpakpraek, 2011; Alves
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and Dumke De Medeiros, 2015), project (Cha et al., 2008), industry (Li et al., 2011; Charmondusit
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et al., 2014), region (Sepp¨al¨aa et al., 2005) and country (Wursthorn et al., 2011; Yu et al., 2013).
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Michelsen et al. (2006) presented a methodology for estimating eco-efficiency in extended supply
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chains based on a case study of furniture production in Norway. A framework for eco-efficiency
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in minerals processing was illustrated with practical examples from gold, base metals, alumina,
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aluminium and pigment operations in Australia (Van Berkel, 2007). Specific to farming, Reith
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and Guidry (2003) applied the lessons of industry eco-efficiency analysis to the agricultural sector
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by measuring the eco-efficiency of Complex in the Model Sustainable Agricultural Complex. The
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eco-efficiency of a New Zealand dairy farm in terms of milk production and land use was compared
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using life cycle assessment methodology (Basset-Mens et al., 2009). By defining eco-efficiency
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on an area basis as the net profit per kg greenhouse gas emissions, M¨uller et al. (2015) estimated
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eco-efficiency for kiwi fruit production in New Zealand.
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The eco-efficiency ratio indicator is simple to calculate and is useful for supporting policy
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decisions in a readily intelligible form. However, due to the variety of economic and environ-
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mental performances, a main challenge in eco-efficiency analysis is how to specify and aggregate
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economic and environmental effects, mainly environmental effects (Huppes and Ishikawa, 2005). 8
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There have been three main ways to address this issue in empirical research. The predominant
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approach is to aggregate the relevant environmental effects into a broadly acceptable single-score
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result based on value judgements or preferences, as most studies have done based on life cycle
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assessment.
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Another way is calculating individual eco-efficiency indicator for various environmental im-
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pacts. Van Caneghem et al. (2010b) proposed a methodology for eco-efficiency reporting with
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eco-efficiency indicators for climate change, acidification, photooxidant formation, human toxic-
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ity, freshwater aquatic ecotoxicity, eutrophication, energy consumption and waste generation. The
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eco-efficiency indicators proposed by Park and Behera (2014) included one economic indicator
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and three generally applicable simplified environmental indicators (raw material consumption, en-
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ergy consumption and CO2 emissions). Van Caneghem et al. (2010a) illustrated the eco-efficiency
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of the steel industry with six partial eco-efficiency indicators for the impact categories acidifica-
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tion, photooxidant formation, human toxicity, freshwater aquatic ecotoxicity, eutrophication and
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water use. Eco-efficiency indicators were calculated as per impact category (environmental im-
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pact indicators based upon life cycle assessment and energy and water use analyses) to analyse
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the eco-efficiency of paddy rice production in north-eastern Thailand (Thanawong et al., 2014). In
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addition to individual environmental impact indicators, various economic performance indicators
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could be calculated separately as well. Cerutti et al. (2013) examined sustainable farming in the
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fruit production systems of the Piemonte Region of Northern Italy based on the quantification of
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four different ecological footprint applications related to different functional units: tons of product,
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nutrient content in the fruit produced, hectare of crops and revenue.
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The third way is the optimization of multiple indicators with a participatory approach, Delphi
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panel or principal components analysis. Mickwitz et al. (2006) proposed regional eco-efficiency
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indicators using a participatory approach. Koskela (2015) discussed the measurement of eco-
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efficiency in the Finnish forest industry. The main method used in this research was the Delphi
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panel, and the eco-efficiency indicators were based on the expert ratings. Principal components
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analysis is an effective approach for aggregating eco-efficiency indicators and assisting decision
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makers by reducing redundancy in an eco-efficiency indicators matrix (Jollands et al., 2004). The
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method has been used to aggregate eco-efficiency indices for New Zealand (Jollands et al., 2004)
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and the United States Manufacturing and Transportation Nexus analysis (Park et al., 2015). Overall, when more than one economic or environmental performance indicator is concerned,
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aggregation into a single numerator or denominator requires appropriate methods and assumptions
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on aggregation weights (Coelli et al., 2005). The frontier approach can generate objective weights
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from the data, and it is considered more reasonable in the context of eco-efficiency measurements
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(Huang et al., 2014).
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2.2. The frontier-based approach
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Production efficiency models estimate frontier functions and measure the efficiencies of firms
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relative to the estimated frontiers (Coelli et al., 2005). The concept can be extended to eco-
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efficiency. There are two main frontier-based approaches: the parametric frontier approach (e.g.
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SFA) and the nonparametric frontier approach (e.g. DEA). The eco-efficiency frontier approach
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was first adopted by Kuosmanen and Kortelainen (2005), which assessed the eco-efficiency of road
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transportation in the three largest towns of eastern Finland using the DEA technique.
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(1) DEA
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Since Kuosmanen and Kortelainen (2005), a number of studies have investigated eco-efficiency
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using DEA. Those studies involved various topics, such as the eco-efficiency of industrial systems
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in China (Zhang et al., 2008), the convergence in eco-efficiency of a group of 22 OECD countries
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(Camarero et al., 2013), the dynamics of regional eco-efficiency in China (Huang et al., 2014),
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manufacturing sectors and the transportation industry of the U.S. (Egilmez and Park, 2014) and
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the eco-efficiencies of ten comparable pesticides (Zhu et al., 2014). Other than the transitional
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DEA model, with several environmental performance indicators as inputs and one economic per-
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formance indicator as the output, some more complicated DEA models have been presented re-
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cently to calculate eco-efficiency. Beltr´an-Esteve et al. (2014) used directional distance functions
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to extend the nonparametric metafrontier approach to efficiency measurements proposed for the
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assessment of technological differences in eco-efficiency between groups of producers. Rashidi
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and Farzipoor Saen (2015) developed a DEA model that divides the inputs into energy and non-
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energy inputs and the outputs into desirable and undesirable outputs. Zhang et al. (2017) estimated
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industrial eco-efficiency in China and analysed its determinants using three-stage DEA.
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Many studies assess farming eco-efficiency using DEA as well. Picazo-Tadeo et al. (2011)
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measured eco-efficiency at the farm level in Spain and studied the determinants of eco-efficiency
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further using truncated regression and bootstrapping techniques. Hoang and Alauddin (2012) pre-
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sented an input-oriented DEA framework. With this framework, the economic, environmental and
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ecological efficiency of OECD agriculture was measured and decomposed. G´omez-Lim´on et al.
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(2012) used DEA techniques and pressure distance functions to contribute a farm-level assessment
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of the eco-efficiency of a sample of 292 Andalusian olive farmers. Beltr´an-Esteve et al. (2017)
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proposed the use of life cycle analysis, a metafrontier directional distance function approach, and
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DEA to assess technological and managerial differences in eco-efficiency between Spanish citrus
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farm systems. Besides, an eco-efficiency analysis of sustainability-certified coffee production in
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Vietnam was conducted by Ho et al. (2018).
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(2) Other frontier-based approaches
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Compared to DEA, the application of other frontier-based approaches in eco-efficiency mea-
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surement has had limited discussion. Quariguasi Frota Neto et al. (2009) proposed a methodology
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based on multi-objective linear programming aimed to handle the visual representation of the eco-
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efficient frontier. Carberry et al. (2013) diagnosed the state of agricultural production in China,
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Zimbabwe and Australia. More than 3,000 surveyed crop yields in these three countries were
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compared against simulated grain yields at farmer-specified levels of nitrogen input (production
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frontiers).
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Notably, SFA, a mainstream approach in efficiency and productivity analyses, is scarcely used
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to estimate eco-efficiency. The main reason is that SFA can only be used for estimating the techni-
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cal efficiency of production with one output indicator. Due to this limitation, Robaina-Alves et al.
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(2015) presented a new stochastic frontier model to assess technical efficiency that combines in-
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formation from the DEA and the structure of composed error from the SFA. In addition, Lauwers
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(2009) considered two different models, environmentally adjusted production efficiency models
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and frontier eco-efficiency models, and attempted to justify incorporating the materials balance
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principle into them.
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The strengths and weaknesses of various methods of eco-efficiency estimation are shown in
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Table 1.To summarize the previous literature, four aspects are highlighted. First, the literature on 11
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farming eco-efficiency is rich; however, few studies have focused on the eco-efficiency of grain
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production at the macro-level. Second, the ratio method and DEA are two popular approaches for
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these calculations. The ratio method is often used in the beginning of eco-efficiency assessments,
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whereas the frontier-based approach has been more popular recently. Third, the single indicator
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separately can only provide limited information. The aggregation of indicators requires appro-
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priate methods. Fourth, SFA is another common efficiency estimation method in productivity
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analyses, and it has some other advantages over DEA. On the one hand, SFA consider statistical
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noise, unlike DEA, which attributes all deviations to inefficiency. On the other hand, as a paramet-
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ric frontier approach, the SFA model involves the influencing factors analysis of eco-efficiency,
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which makes it easy to ascertain policy variables that can be used to address eco-inefficiency.
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However, few studies have used it to measure eco-efficiency so far. The reasons are as follows: 1)
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SFA is a parametric frontier approach based on production function. Environmental performance
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cannot be seen as the input of economic performance in general. 2) The first problem could be
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handled by considering environmental impact as an undesirable output and economic performance
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as a desirable output, as has been done in many improved DEA models. However, it cannot be im-
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plemented because SFA can only analyse one-output models. Including green, blue and grey WFs
324
as inputs in the production function can make estimating eco-efficiency using SFA reasonable and
325
suitable, at least for grain production.
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308
According to these literatures review, scarce studies have analysed and evaluated eco-efficiency
327
using SFA, particularly for grain production at the regional level. In light of this gap in the liter-
328
ature and the relevance of this topic, a WF-SFA framework is proposed and applied to analyse
329
the regional eco-efficiency of grain production in China in this study. This research can provide
330
a basic framework on eco-efficiency evaluation of the grain production with the SFA technique,
331
which will feed into policy and strategic development.
332
3. Methodology and data
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333
This research applies two methods. First, the rainwater consumption (green WF), irrigation
334
water consumption (blue WF) and pollutant-assimilating water consumption (grey WF) related to
335
grain production are calculated based on a WF analysis. Second, a grain production SFA model 12
Table 1 Strengths and weaknesses of various methods of eco-efficiency estimation. The ratio method
The frontier-based approach
Method Aggregated indi-
Data
tor
cator
analysis (DEA)
Economic per f ormance Environmental in f luence
definition Features
envelopment
Stochastic
frontier
approach
SC
Single indica-
(SFA)
Actual output Frontier output
M AN U
Eco-efficiency
RI PT
ACCEPTED MANUSCRIPT
It is often com-
It is nonparametric
It is parametric and stochastic,
bined with life cy-
and
using the econometric approach.
cle analyses.
using the mathematical
deterministic,
programming
It is straightfor-
and intuitive.
It is difficult
AC C
Weaknesses
It is simple
1) It does not require
1) It offers a richer specification;
ward and compre-
any distributional as-
2) it allows for a formal statisti-
hensive.
sumptions about effi-
cal testing of hypotheses and the
ciency, and 2) it can
construction of confidence inter-
address the joint pro-
vals; and 3) the random effects
duction of multiple
can be separated from the contri-
outputs.
bution of variation in efficiency.
It does not consider
It is inflexible in addressing
statistical noises.
multiple outputs and the some-
EP
Strengths
TE D
method.
Aggregating
the
to specify a
performances into
representative
one
meaningful
what arbitrary distribution as-
performance
indicator is often
sumptions regarding the ineffi-
indicator.
not possible.
ciency term.
13
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that considers three WFs (accounting for resources consumption and environmental impact) as
337
inputs is presented to assess eco-efficiency, identify the factors influencing eco-efficiency and cal-
338
culate the output elasticities of three WFs.
339
3.1. Method of assessing the grain production water footprint
340
341
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336
The total WF of grain production is the sum of the green, blue and grey water components (Hoekstra et al., 2011):
SC
T WF = GWF + BWF + EWF.
(1)
Where T WF is the total water footprint of grain production, m3 ; GWF is the green water footprint,
343
m3 ; BWF is the blue water footprint, m3 ; and EWF is the grey water footprint, m3 .
344
345
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342
Following Hoekstra et al. (2011), the green WF related to grain production is calculated as follows:
Pree · S . λ
(2)
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GWF = 346
Where Pree is the effective precipitation during the growth period of grain, mm; S is the sown area
347
of grain, ha; and λ is the multiple crop index of grain. The effective precipitation during the growth period of grain is calculated according to the
349
method developed by the USDA; effective rainfall can be calculated according to Doll and Siebert
350
(2002):
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348
351
352
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Pre · (125 − 0.2Pre) 125 Pree = 125 + 0.1Pre
Pre ≤ 250mm (3) Pre > 250mm
Where Pre is monthly precipitation, mm. The blue WF related to grain production is calculated as follows (Sun et al., 2016): BWF = I · S .
353
Where I is irrigation water use per unit of sown area of grain, m3 /ha. 14
(4)
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355
According to Hoekstra et al. (2011), the grey WF related to grain production is calculated as follows: EWF =
X i
! Poli · αi . Cimax − Cinat
(5)
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354
Where Pol is the consumption of pollutants, which consist of fertilizers, pesticides and insecti-
357
cides, kg; α is the leaching-run-off fraction, %; Cmax is the maximum acceptable concentration for
358
a pollutant, kg/m3 ; and Cnat is the natural concentration of a pollutant, kg/m3 .
359
3.2. Stochastic frontier production function model and eco-efficiency
SC
356
SFA is a parametric frontier approach developed independently by Aigner et al. (1977) and
361
Meeusen and van Den Broeck (1977). SFA starts with a standard cost or profit function and esti-
362
mates the minimum cost or maximum profit frontier for the entire sample from balance sheet data.
363
The efficiency measure for a specific observation is its distance from the frontier. Kumbhakar et al.
364
(1991) and Reifschneider and Stevenson (1991) proposed single-stage stochastic frontier function
365
models in which technical inefficiency effects were involved. Battese and Coelli (1992) and Bat-
366
tese and Coelli (1995) extended the previous two models for panel data. The decisive virtues of
367
SFA are that it covers both random noise, e.g., due to well-known measurement problems, and
368
systematic differences, e.g., due to heterogeneity across samples (Kumbhakar and Lovell, 2000).
369
These features allow for a relative comparison of markedly different samples. SFA is now a popu-
370
lar tool for benchmarking efficiency and productivity in many fields.
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360
Following Battese and Coelli (1995), a SFA model for grain production is established. The
372
schematic diagram of this model is as shown in Fig. 1. In the tth year, for the ith region, the basic
373
stochastic frontier production function is as follows:
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371
yit =exp(Xit β + vit − uit ), uit =Z it δ + wit .
(6)
374
Where yit denotes the production obtainable from Xit , a vector of values of inputs, and β is an
375
unknown parameter vector to be estimated. vit s are assumed to be iid N(0, σ2v ) random errors, 15
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Eco-efficiency:
Inputs,QGLFDWRUV:
the ratio of the actual output to the potential output.
Capital (K)
Output,QGLFDWRU:
Labour (L) Green water footprint (GWF)
Influence factors: Per capita GDP (PG) Grey water footprint (EWF)
Per capita water supply (PWS) Proportion of the irrigation area in the total cultivated area (IS)
Proportion of government expenditure on environmental protection relative to total government expenditure (ES)
M AN U
annual average temperature (T)
Proportion of the disaster area in the total sown area (DS)
SC
Blue water footprint (BWF)
RI PT
Output value of grain (Y)
Fig. 1. Framework of the stochastic frontier function model for grain production.
independently distributed of uit . uit s are non-negative random variables associated with the tech-
377
nical inefficiency of production and are assumed to be independently distributed, such that uit is
378
obtained by truncation of the normal distribution with zero mean, Z it δ, and variance, σ2u . Z it is a
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376
vector of explanatory variables associated with technical inefficiency, and δ is a vector of unknown
380
coefficients. The random variable, wit , is defined by truncation of the normal distribution with zero
381
mean and variance, σ2u , such that uit is a non-negative truncation of the N(Z it δ, σ2u ) distribution as
382
assumed. The disturbance uit reflects the fact that each output must lie on or below its frontier
383
Xit β + vit . The frontier is stochastic, with random disturbance vit being the result of favourable
384
as well as unfavourable external events such as climate, topography, and machine performance
385
(Aigner et al., 1977).
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386
Therefore, the eco-efficiency (the technical efficiency of ecological production) for the ith re-
387
gion at the tth observation point can be defined as the ratio of the actual output to the frontier
388
output: Eit =
389
exp(Xit β + vit − uit ) = exp(−uit ). exp(Xit β + vit )
(7)
This study specifies the stochastic frontier production function using the flexible translog spec16
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390
ification. The linearized version of the tanslog time-varying stochastic frontier production function
391
to be estimated is as follows:
j=1
5
5
5
1 1 XX 1X β jk ln x jit ln xkit + βtt t2 + β j ln x jit + βt t + β jt ln x jit t + vit − uit . (8) 2 j=1 k≥ j 2 2 j=1
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ln yit = β0 +
5 X
Where ln denotes the natural logarithm; x1 is the capital (K); x2 is labour (L); x3 is the green water
393
footprint (GWF); x4 is the blue water footprint (BWF); x5 is the grey water footprint (EWF); and
394
t is the time trend variable. The inefficiency effects are assumed to be defined by
M AN U
395
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392
uit = δ0 + δ1 PGit + δ2 PWS it + δ3 IS it + δ4 DS it + δ5 ES it + δ6 T it + δt t + wit .
(9)
Where PG is per capita GDP; PWS is the per capita water supply; IS is the proportion of the
397
irrigation area in the total cultivated area; DS is the proportion of the disaster area in the total
398
sown area; ES is the proportion of government expenditure on environmental protection relative
399
to total government expenditure; and T is the annual average temperature.
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396
The descriptions of the variables used in the grain production SFA model are shown as Table
401
2. The maximum likelihood method is used for simultaneous estimation of the parameters of
402
the stochastic frontier and the model for the inefficiency effects, defined by Eqs. 8 and 9. The
403
likelihood function is expressed in terms of the variance parameters, σ2 = σ2v + σ2u and γ = σ2u /σ2 .
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400
σ2 is the total variances of the error terms. γ is the ratio of variance in the inefficiency effects to
405
the total variances of the error terms.
406
3.3. Study area and data description
407
408
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The data used in this paper include a balanced panel consisting of annual time series for 31 provinces, autonomous regions and municipal cities in mainland China from 1997 to 2015.
409
In this paper, grain includes the three main crops: rice, wheat and maize. Only the consumption
410
of nitrogen fertilizer was considered for calculating the grey WF. Among the data used to estimate
411
the WF of grain production, the sown area of crops, the multiple crop index, the consumption
412
of nitrogen fertilizer and the total sown area were obtained from the Chinese National Bureau 17
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Table 2 Description and summary statistics for variables in the stochastic frontier production functions. Variable
Description
N
Mean
SD
Min
P50
Max
Output value of grain (billion yuan)
589
36.68
44.87
0.13
20.34
276.10
K
Capital (billion yuan)
589
14.04
16.49
0.06
7.96
93.25
L
Labour (million man − days)
589
376.00
309.10
3.14
338.40
1,900.00
GWF
Green water footprint (billion m3 )
589
9.62
7.98
0.11
8.79
37.01
BWF
Blue water footprint (billion m3 )
589
7.06
5.49
0.10
6.63
27.90
EWF
Grey water footprint (billion m3 )
589
9.79
8.63
0.07
7.10
39.22
PG
=
589
23,911.00 20,961.00 2,250.00 16,469.00 107,960.00
PWS
=
589
386.20
IS
=
DS
=
ES
=
T
Annual average temperature (◦C)
SC 423.50
M AN U
GDP Population (yuan/capita) Water supply 3 Population (m /capita) Irrigation area T otal cultivated area (%)
RI PT
Y
Disaster area T otal sown area (%) Government expenditure on environmental protection T otal government expenditure
(%)
40.00
276.10
2,657.00
589
55.10
23.03
17.37
57.71
100.00
589
14.39
10.35
0.00
11.92
62.31
589
1.42
1.67
0.00
0.00
6.73
589
14.36
5.04
4.30
15.10
25.40
of Statistics (1998-2016). The precipitation data were obtained from the China Meteorological
414
Data Service Center (1997-2015). The water consumption data were obtained from the Chinese
415
National Bureau of Statistics and Ministry of Environmental Protection (1998-2016). It is assumed
416
that on average 10% of the applied nitrogen fertilizer was lost through leaching or runoff, following
417
Zhang et al. (2015). The standard recommended by US-EPA is 10 mg per litre measured as nitrate-
418
nitrogen. The natural nitrogen concentrations were assumed to be zero.
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413
The total output value, total capital input and labour associated with grain were calculated
420
as the sum of the per ha values of the three main crops, rice, wheat and maize, weighted by
421
their sown area. The output value of the three main crops per ha, the capital input per ha for
422
the three crops (which includes the seed, manure, chemical fertilizer, film, pesticides, animal
423
power, mechanical power, irrigation and drainage, fuel, materials and other) and the labour per ha
424
for the three crops were obtained from the Price Division of Chinese National Development and
425
Reform Commission (1998-2016). The sown areas of the three main crops were obtained from
426
the Chinese National Bureau of Statistics (1998-2016). The output value and capital input of grain
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419
18
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427
were adjusted to values based on the 1997 price level by dividing by the agricultural production
428
price index and agricultural materials price index, which were obtained from the Chinese National
429
Bureau of Statistics (1998-2016). Among the possible factors that influence eco-efficiency, GDP and population (used to calcu-
431
late per capita GDP), the cultivated area (used to calculate the proportion of the irrigation area
432
in the total cultivated area), total sown area (used to calculate the proportion of the disaster area
433
in the total sown area), government expenditure on environmental conservation and total govern-
434
ment expenditure (used to calculate the proportion of government expenditure on environmental
435
protection relative to total government expenditure) and average annual temperature of the capital
436
city were obtained from the Chinese National Bureau of Statistics (1998-2016). The irrigated area
437
(used to calculate the proportion of the irrigation area in the total cultivated area) were obtained
438
from the Chinese Ministry of Agriculture (1998-2016). The water supply (used to calculate per
439
capita water supply) and the disaster area (used to calculate the proportion of the disaster area in
440
the total sown area) were obtained from the Chinese National Bureau of Statistics and Ministry
441
of Environmental Protection (1998-2016). Some data on the government expenditure on environ-
442
mental conservation from 1997 to 2006 were missing and filled by the difference interpolation
443
method. Table 2 shows summary statistics for the SFA model variables as well.
444
4. Results
445
4.1. Water footprint of grain production
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430
With the method presented in Section 3.1, the WFs of grain production in China were calcu-
447
lated. Fig. 2 shows the change in the grain production WF during the period 1997-2015. The grain
448
production WF exhibited a significant decrease before reaching a minimum value (714.60 billion
449
m3 ) in 2003. Since then, the grain production WF exhibited a slowly increasing trend. The reason
450
is that policies to encourage grain production, direct grain subsidy and exemption from agricul-
451
tural tax, are implemented since 2004. The grain production WF in 2015 was 894.72 billion m3 ,
452
which was 1.06 times that of the 1997 value. The trend is parallel with the change in the total
453
grain yield over the examined period. However, the total grain yield increased by a factor of 1.27
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446
19
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454
from 841.76 billion kg in 1997 to 894.72 billion kg in 2015. This implies that the water resource
455
use per grain production (unit WF of grain production) in China has decreased in recent years. Blue water footprint
Grain yield
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600
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Water footprint (billion m3)
800
400
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200
0
Grey water footprint
1000
750
500
Grain Yield (billion kg)
Green Water footprint
RI PT
1000
250
0
1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015
Fig. 2. Water footprint of grain production and grain yield in China.
The annual average grain production WF in China from 1997 to 2015 was 820.37 billion m3 :
457
the green WF was 36.36% (298.26 billion m3 ), the blue WF was 26.66% (218.72 billion m3 ), and
458
the grey WF was 36.98% (303.39 billion m3 ). The green, blue and grey WFs were steady over the
459
study period, with average rates of increase of 1.07%, 0.15% and 0.29%, respectively.
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460
The spatial distribution of the WF of crop production is shown in Fig. 3 and Fig. 4. The
461
provincial grain production WFs decreased from eastern to western China. This geographical
462
distribution feature of grain production WF is consistent with the regional climate, soil and natural
463
resources. Over the period 1997-2015, Henan was the province with the highest virtual water
464
consumption for grain production (71.43 billion m3 /year), followed by Jiangsu (64.90 billion
465
m3 /year) and Shandong (58.22 billion m3 /year). The three largest consumers are all located in 20
ACCEPTED MANUSCRIPT
466
the east. Tibet, Qinghai and Beijing consumed the smallest amount of virtual water for grain
467
production, with annual average WFs of 0.50, 1.17 and 2.30 billion m3 /year, respectively. 50
50 Heilongjiang
Heilongjiang
Jilin Xinjiang Inner Mongoria Beijing Tianjin Hebei Shanxi
GansuNingxia
Xinjiang
Liaoning
Inner Mongoria Beijing Tianjin Hebei Shanxi
GansuNingxia
Shandong
Qinghai
Liaoning
Shandong
Qinghai
Shaanxi
30
Henan
Tibet
Jiangsu Anhui Shanghai
Hubei
Sichuan Chongqing
Jiangxi Fujian
Zhejiang
Hunan
Guizhou
Yunnan
Jiangxi Fujian
Yunnan
Guangxi
20
Henan
Tibet
Zhejiang Hunan
Guizhou
Shaanxi
30
Jiangsu Anhui Shanghai
Hubei Sichuan Chongqing
Guangxi
20
Guangdong
Hainan
Guangdong
Hainan
Water footprint (2001-2005)
Water footprint (2006-2010)
60 10
Jilin
40
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40
50
10
40
60 40
20
20
100
Heilongjiang
120
80
100
120
SC
80
Jilin
40 Xinjiang
Liaoning Inner Mongoria
M AN U
Beijing Tianjin Hebei
Shanxi
Gansu Ningxia Qinghai
Shaanxi
30 Tibet
Henan
Shandong
Jiangsu Anhui Shanghai
Hubei
Sichuan
Chongqing
Zhejiang
Hunan
Guizhou
Jiangxi
Fujian
Yunnan
Guangxi
20
Hainan
Heilongjiang
Jilin
40 Xinjiang Inner Mongoria Beijing Tianjin Hebei Shanxi
GansuNingxia
Liaoning
Shaanxi
Henan
Tibet
Jiangsu Anhui Shanghai
Hubei Sichuan Chongqing
Zhejiang Hunan
Guizhou
Jiangxi Fujian
Yunnan Guangxi
20
Inner Mongoria Beijing Tianjin Hebei
20
100
80
EP
40
120
Shanxi
GansuNingxia
Liaoning
Shandong
Qinghai Shaanxi
30
Henan
Tibet
60
Jiangsu Anhui Shanghai
Hubei Sichuan Chongqing
Guizhou
Zhejiang Hunan
Jiangxi Fujian
Yunnan Guangxi
20
Guangdong
Hainan
Water footprint (2011-2015)
20
60 10
Jilin Xinjiang
40
Guangdong
Hainan
Water footprint (1997-2000)
80
Heilongjiang
40
Water footprint (1997-2015)
Shandong
Qinghai
10
50
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50
30
Guangdong
10
60 40 20
100
120
80
100
120
AC C
Fig. 3. Annual average water footprint of grain production.
468
Fig. 3 also shows the temporal evolution characteristics of provincial grain production WFs.
469
Comparing the WFs in the last 5 years (2011-2015) with those in the earliest 4 years (1997-
470
2000), the spatial distribution of the WFs in China has changed greatly. The three regions with
471
the largest decrease in grain production WFs were Beijing, Shanghai and Zhejiang. Their grain
472
production WFs decreased by 60.23%, 54.88% and 49.42%, respectively, from the earliest 4 years
473
to the last 5 years, and the biggest parts of these decreased occurred in 2001-2005. The three 21
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0
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2.3 2.7 3.3 0.5 1.2 4.6 6 10.3 10.6
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Ti b Q et in g Be hai i Sh jing an Ti gha an i H jin ai n N an in gx Sh ia a G nxi a C ns ho u ng Fu qin g G jian ui zh Z In he ou ne jia r M ng Xi ong nj o Sh iang ria a Li anx ao i n Ji ing an Yu gxi nn a Ji n G lin u G an ua gx ng i Si do ch ng u H an ub H ei H un ei an lo ng H jian eb g A ei Sh nhu an i Ji don an g g H su en an
The share of various water footprint (%) 75
50
23.3 23.6 28.4 28.4 28.6
M AN U
Total water footprint Green Water footprint
11.9
14.2 15.2
22
RI PT
Blue water footprint
39.2
34.5 35.5
58.2
36.7
17.5
Fig. 4. Provincial annual average water footprint and its distribution from 1997 to 2015. 60
44.7 44.8 47.4 48.6
41.5
40
20.5
20
0
Total water footprint (billion m3)
100
SC
ACCEPTED MANUSCRIPT
Grey water footprint
71.4
80
64.9
ACCEPTED MANUSCRIPT
regions are all rapidly developed industrial regions in China. Due to uneven development and
475
industrial distribution, in some industrially non-rapid developed provinces, such as Heilongjiang,
476
Inner Mongolia and Shanxi, crop production and related crop production WFs have grown rapidly
477
since 2006. The average annual WFs of grain production in Heilongjiang, Inner Mongolia and
478
Shanxi have increased from 25.95, 14.62 and 8.49 m3 /year in the period 2001-2005 to 71.13,
479
29.03 and 12.83 m3 /year in the period 2011-2015, respectively.
RI PT
474
The proportions of green, blue and grey WFs relative to the total, averaged over the period
481
1997-2015, were very different among provinces (Fig. 4 and 5). A high proportion of the green
482
WF relative to the total reflects the presence of intensive rain-fed farming. Guizhou had the high-
483
est green WF proportion (54.38%), followed by Heilongjiang (49.87%) and Anhui (48.44%). In
484
semi-arid northwest China, the green WF proportions were less than 20%; Xinjiang had the lowest
485
proportion (9.05%), followed by Ningxia (15.98%). However, a high proportion of blue WF rela-
486
tive to the total reflects the presence of intensive irrigated agriculture. In the semi-arid northwest,
487
the blue WF proportions exceeded 50%: Xinjiang had the highest proportion (64.53%), followed
488
by Ningxia (52.92%) and Tibet (51.97%). The three provinces with the lowest blue WF propor-
489
tions were Chongqing (11.36%), Henan (13.60%) and Shaanxi (15.57%). Some of the regions
490
with a low green or blue WF proportion had a high grey WF proportion, which implies that high
491
grey WF proportions exist in those regions, such as Beijing, Shanxi and Shaanxi, i.e., 52.50%,
492
51.01% and 50.58%, respectively. The three provinces with the lowest grey WF proportions were
493
Tibet (17.04%), Qinghai (18.89%) and Guangxi (20.84%).
494
4.2. Estimates of grain production stochastic frontier model
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480
495
Maximum likelihood estimates of the parameters of the grain production stochastic frontier
496
model were obtained using a modification of the computer program FRONTIER 2.0 (Coelli, 1996).
497
Because the model involves a large number of parameters, tests of several null hypotheses were
498
first considered to decide whether a simpler model would be an adequate representation of the data
499
(see Table 3). The hypotheses were tested using likelihood ratio tests. The likelihood ratio test
500
statistic is λ = −2[L(H0 ) − L(H1 )], where L(H0 ) and L(H1 ) are the values of the log-likelihood
501
function under the specifications of the null and alternative hypotheses, H0 and H1 , respectively. 23
ACCEPTED MANUSCRIPT
50 Heilongjiang
Jilin
40 Xinjiang
Liaoning Inner Mongoria Beijing Tianjin Hebei
Tibet
Shanxi
Shandong
Shaanxi Henan Jiangsu AnhuiShanghai Hubei Chongqing Sichuan Zhejiang Hunan Jiangxi Guizhou Fujian Yunnan GuangxiGuangdong
20
Hainan
Green water footprint proportion (%)
80
100
120
M AN U
50
SC
50 40 30 20 10
10
RI PT
Gansu Ningxia Qinghai
30
Heilongjiang
Jilin
40
Xinjiang
Liaoning Inner Mongoria Beijing Tianjin Hebei
Gansu Ningxia
Qinghai
30
Tibet
Shanxi
Shandong
Shaanxi Henan Jiangsu AnhuiShanghai Hubei Chongqing Sichuan Zhejiang
Hunan Jiangxi Guizhou Fujian Yunnan GuangxiGuangdong
20
TE D
Hainan
Blue water footprint proportion (%)
10
60 50 40 30 20
80
100
120
EP
50
Heilongjiang
Jilin
AC C
40
Liaoning Inner Mongoria Beijing Tianjin Hebei Gansu Ningxia Qinghai
30
Tibet
Shanxi
Shandong
Shaanxi Henan Jiangsu AnhuiShanghai Hubei Chongqing Sichuan Zhejiang Hunan Jiangxi Guizhou Fujian Yunnan GuangxiGuangdong
20
10
Xinjiang
Hainan
Grey water footprint proportion (%) 50 40 30 20 80
100
120
Fig. 5. The proportion of green, blue and grey water footprint relative to the total (%).
24
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502
If the null hypothesis is true, then λ has an approximate Chi-square distribution with degrees
503
of freedom equal to the number of restrictions. If the null hypothesis includes λ = 0, then the
504
asymptotic distribution is a mixed Chi-square distribution (Coelli, 1996).
RI PT
Table 3 Statistics for tests of hypotheses involving some coefficients of the stochastic frontier function. Hypothesis
Log-likelihood
Test statistics
(2)
(3)
(4)
320.401
H0 : γ = δ 0 = δ j = δ t = 0
229.257
H1 : β jk , 0 or βtt , 0 or β jt , 0
320.401
H0 : β jk = βtt = β jt = 0
Decision
182.288
20.972
Reject
113.676
20.972
Reject
31.104
20.972
Reject
12.894
20.972
Accept
170.384
8.273
Reject
263.563
H1 : βtt , 0 or β jt , 0
320.401
H0 : βtt = β jt = 0
304.849
H1 : β jt , 0
320.401
H0 : β jt = 0
313.954
H1 : β jt = 0 and δ0 , 0 or δ j , 0 or δt , 0 H0 : β jt = 0 and δ0 = δ j = δt = 0
313.954
228.762
TE D
(5)
H1 : γ , 0 or δ0 , 0 or δ j , 0 or δt , 0
M AN U
(1)
value
(1% level)
SC
function
Critical
The first hypothesis considered in Table 3 is that the inefficiency effects are not present in the
506
model (γ = δ0 = δ j = δt = 0). This null hypothesis was strongly rejected. The results suggest
507
that ecological inefficiency exists and that the SFA model is suitable. The second hypothesis, that
508
the second-order coefficients in the translog function are equal to zero (β jk = βtt = β jt = 0) and
509
hence that the Cobb-Douglas function applies, was rejected as well. Thus, the translog stochastic
510
frontier production function model provides an accurate specification for grain production data
511
in China. The third hypothesis, i.e., that the technical efficiency of ecological production does
512
not vary over time (βtt = β jt = 0), was rejected at the 1% significance level, while the fourth
513
514
AC C
EP
505
hypothesis, i.e., that the technical progress of ecological production is neutral (β jt = 0), was P accepted. This result suggests that deleting 21 5j=1 β jt ln x jit t from the original model defined by
515
Equation 8 makes the model simpler but still adequate as a representation of the data. With it,
516
as the alternative hypothesis, the fifth null hypothesis specifies that the inefficiency effects are not 25
ACCEPTED MANUSCRIPT
a linear function of selected influencing factors (δ0 = δ j = δt = 0). This null hypothesis was
518
rejected at the 1% level of significance, which indicates that the joint effects of these 7 explanatory
519
variables on the inefficiencies of ecological production are significant, although the individual
520
effects of one or more of the variables may not be significant. These results above imply that the
521
522
RI PT
517
model for describing the grain production in China cannot be adequately specified in terms of a P simpler model associated with these null hypotheses, except that 21 5j=1 β jt ln x jit t could be deleted, given the specifications of the translog stochastic frontier grain production model defined by Eqs.
524
8 and 9. The estimated results for this final selected model are presented in Table 4.
SC
523
As shown in Table 4, the γ value was 0.608, and it was significant at the 1% level. This finding
526
indicates that 60.8 percent of the variation in grain output can be attributed to the technical ineffi-
527
ciency of ecological production. It also confirmed the presence of the one-sided error component
528
in the model, thus rendering the use of the OLS estimation technique was inadequate in represent-
529
ing the data. In addition, σ2 was 0.028 and significant, indicating the correctness of the specified
530
assumptions of the distribution of the composite error term. These results imply that the model is
531
appropriate and creditable.
532
4.3. Eco-efficiency levels of grain production
TE D
M AN U
525
Grain production eco-efficiencies in China over the study period were estimated with Eq. 7
534
based on the SFA model results shown in Table 4. The results indicate that the eco-efficiencies
535
were within the range of 0.424-0.986 (Fig. 6), with an average value of 0.807. The closer the
536
value of eco-efficiency is to 1, the more efficient the region is, which means that the best utilize of
537
resources is to produce the maximum amount possible while minimizing the environmental impact
538
through pollutant emissions (Robaina-Alves et al., 2015). The results indicate that there is room
539
for China to improve its grain production eco-efficiency.
AC C
EP
533
540
The estimates of eco-efficiency varied considerably across both time periods and regions (Fig.
541
7 and 8). The average grain production eco-efficiency for the whole country decreased in the early
542
sample periods but subsequently improved, and this trend of fluctuating improvement was also
543
observed in east, central and west China. East China has a higher grain production eco-efficiency
544
than central and west China. 26
ACCEPTED MANUSCRIPT
Table 4
Variable
Parameter
Estimate
t-ratio
ln GWF · ln EWF
Stochastic Frontier Production Function: Constant
β0
0.781
ln K
β1
ln L
∗
Variable
RI PT
Results of the stochastic frontier production function model. Parameter
Estimate
t-ratio
β35
-0.334∗∗∗
-2.757
β44
-0.009
-0.221
β45
0.193∗∗
2.131
β55
-0.172∗
-1.934
βtt
0.004∗∗∗
4.619
1.765
ln BWF · ln BWF
1.160∗∗∗
9.373
ln BWF · ln EWF
β2
0.063
0.293
ln EWF · ln EWF
ln GWF
β3
0.077
0.338
t2
ln BWF
β4
0.181
1.394
Inefficiency Model:
ln EWF
β5
-0.605∗∗∗
-4.047
Constant
δ0
0.19726∗
t
βt
-0.023∗∗
-2.209
PG
δ1
-0.00001∗∗∗
-6.561
ln K · ln K
β11
0.041
PWS
δ2
-0.00046
∗∗∗
-8.621
ln K · ln L
β12
-0.220∗∗∗
IS
δ3
0.00003
ln K · ln GWF
M AN U
1.019
-3.807
β13
0.156
∗∗
2.399
DS
ln K · ln BWF
β14
-0.092∗
-1.821
ln K · ln EWF
β15
ln L · ln L
β22
ln L · ln GWF
β23
ln L · ln BWF
β24
ln L · ln EWF
β25
ln GWF · ln GWF
β33
β34
0.00789
6.353
ES
δ5
-0.03647∗∗
-2.428
T
δ6
∗∗∗
0.00859
2.616
δt
0.01637∗
1.716
0.028∗∗∗
10.432
∗∗∗
11.012
TE D
δ4
0.018
0.320
t
0.022
0.197
Variance Parameter:
-1.952
σ2
4.585
γ
0.608
0.056
0.711
Log-likelihood fuction
313.954
0.007
0.109
LR test
189.39
-0.118∗
0.362
∗∗∗
0.041
∗∗∗
AC C
Note:
1.285
EP
ln GWF · ln BWF
0.086
SC
1.889
1. Y: output value of grain; K: capital; L: labour; GWF: green water footprint; BWF: blue water footprint; EWF: grey water footprint; t: time; PG: per capita GDP; PWS : per capita water supply; IS : proportion of the irrigation area in the total cultivated area; DS : proportion of the disaster area in the total sown area; ES : proportion of government expenditure on environmental protection relative to total government expenditure; T : annual average temperature. 2. σ2 : total variances of the error terms. 3. γ: ratio of variance in the inefficiency effects to the total variances of the error terms. 4.
∗∗∗
: significant at 1%, ∗∗ : significant at 5%, ∗ : significant at 10%.
27
ACCEPTED MANUSCRIPT
5%
RI PT
4%
Density
3%
2%
SC
1%
0%
M AN U
0.40 0.45 0.50 0.55 0.60 0.65 0.70 0.75 0.80 0.85 0.90 0.95 1.00
Fig. 6. Distribution of grain production eco-efficiency in China.
0.9
TE D
0.8
0.716
EP
0.751
0.7
0.65
0.6
0.933 0.916 0.906
0.876
0.873
0.841
0.808
0.795
AC C
Eco-efficiency
0.881
0.936 0.935 0.926
0.725
East China
0.647 0.622
Central China
West China
China
0.595
1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015
Fig. 7. Grain production eco-efficiency from 1997 to 2015.
28
50
50 Heilongjiang
Heilongjiang
Jilin
40 Xinjiang
Jilin
40 Xinjiang
Shanxi
Gansu Ningxia Qinghai
Shaanxi
Hubei SichuanChongqing
30
Jiangsu Anhui Shanghai
Shaanxi
Henan
Tibet
Hubei SichuanChongqing
Zhejiang
Hunan Jiangxi Fujian
Guizhou
Shandong Jiangsu Anhui Shanghai Zhejiang
Hunan Jiangxi Fujian
Yunnan
Guangxi
20
Guangdong
Hainan
Eco-efficiency (2001-2005)
Guangxi
M AN U
Yunnan
20
Shanxi
Gansu Ningxia
Shandong
Qinghai
Henan
Tibet
Guizhou
Liaoning Inner Mongoria Beijing Tianjin Hebei
SC
Liaoning Inner Mongoria Beijing Tianjin Hebei
30
RI PT
ACCEPTED MANUSCRIPT
Eco-efficiency (2006-2010)
0.9
Guangdong
Hainan
0.95
10
0.8
10
0.90 0.85
0.7
0.80
0.6 80
100
120
50
80
100
120
50
Heilongjiang
TE D
Heilongjiang
Jilin
40 Xinjiang
Shanxi
Gansu Ningxia Qinghai
Shaanxi
30 Tibet
Henan
Hubei SichuanChongqing Guizhou Yunnan
Jiangsu Anhui Shanghai
AC C
0.7
100
Henan
Tibet Hubei SichuanChongqing Guizhou
Shandong Jiangsu Anhui Shanghai Zhejiang
Hunan Jiangxi Fujian
Yunnan Guangxi
Guangdong
Hainan
Eco-efficiency (2011-2015) 0.95 10
0.90 0.85
0.6
80
Shaanxi
20
Guangdong
Shanxi
Gansu Ningxia Qinghai
30
Zhejiang
EP
10
Liaoning Inner Mongoria Beijing Tianjin Hebei
Shandong
Hainan
Eco-efficiency (1997-2000) 0.8
Xinjiang
Hunan Jiangxi Fujian
Guangxi
20
Jilin
40
Liaoning Inner Mongoria Beijing Tianjin Hebei
0.80 120
80
100
Fig. 8. Annual average grain production eco-efficiency in provinces of China.
29
120
ACCEPTED MANUSCRIPT
The spatiotemporal evolution of grain production eco-efficiency is shown in Fig. 8. The em-
546
pirical evidence shows that the provincial grain production eco-efficiencies have improved signif-
547
icantly over the study period, especially in the last 5 years (2011-2015). In the earliest 4 years
548
(1997-2000), Shanghai (0.873), Xinjiang (0.768) and Zhejiang (0.762) were the three most effi-
549
cient provinces while Shaanxi (0.561), Hubei (0.576) and Shanxi (0.588) constituted the least effi-
550
cient provinces. This current pattern has changed greatly. The eco-efficiencies of Xinjiang (0.985)
551
and Shanghai (0.980) have steadily improved, and they were still two most efficient provinces
552
in the last 5 years (2011-2015). Some other provinces have made great progress and achieved
553
very high eco-efficiencies, such as Jiangsu (0.977), Inner Mongolia (0.977) and Beijing (0.976).
554
In addition to Inner Mongolia (increased by 0.327), Hubei (increased by 0.361), Jilin (increased
555
by 0.327) and Tibet (increased by 0.324) were the other provinces with the largest increases in
556
grain production eco-efficiency. In contrast, small improvement in the eco-efficiencies in Hainan,
557
Guizhou and Yunnan (increased by 0.112, 0.110 and 0.160, respectively) made them become the
558
three least efficient provinces in the period 2011-2015.
559
4.4. Determinants of grain production eco-efficiency
TE D
M AN U
SC
RI PT
545
The eco-efficiency indicator provides the overall outcome for the economic and environmental
561
efficiency of the joint use of production factors. It is also important to know what factors underline
562
the good or poor performance of the provinces mentioned. Given the model for the inefficiency
563
effects, defined by Eq. 9, the δ estimates are of particular interest. The maximum likelihood
564
estimates of the determinants of the eco-efficiency of grain production are presented in Table 3.
565
The results indicate that the grain production eco-efficiency tended to be higher for regions with
566
higher per capita GDP, greater per capita water supply, and a higher proportion of government
567
expenditure on environmental protection. The grain production eco-efficiency will increase with
568
a decrease in the proportion of disaster areas and temperature. The coefficients of the proportion
569
of the irrigation area relative to the total cultivated area had positive signs but were not significant.
570
The positive coefficient for t suggests that the ecological inefficiencies of grain production tended
571
to increase throughout the study period when other variables were controlled.
AC C
EP
560
30
ACCEPTED MANUSCRIPT
572
5. Discussions
573
5.1. Comparison of the results Although there is no known research calculating the eco-efficiency of grain production using
575
the SFA technique, many researchers have analysed eco-efficiency related to farming at different
576
levels. As shown in Table 5, the subjects of these studies are different. Thus, the results cannot be
577
compared directly, especially with eco-efficiency defined as a relative comparison indicator, as in
578
this study.
RI PT
574
This paper introduced a way of measuring eco-efficiency based on the SFA technique. The ratio
580
method and DEA are the two main methodologies used in previous studies. The ratio methodology
581
proposes a single efficiency measure to select one solution out of a set of solutions according to
582
the highest (economic value / environmental pressure) ratio (Quariguasi Frota Neto et al., 2009).
583
However, it cannot differentiate between different environmental impacts, and it requires subjec-
584
tively assigned weights by the estimator. In mathematical terms, the ratio procedure is a DEA
585
model with one input (environmental performance), one output (economic performance) and con-
586
stant returns of scale (Quariguasi Frota Neto et al., 2009). DEA can provide a more comprehensive
587
result for eco-efficiency evaluation when more than one input or output is considered, as Hoang
588
and Alauddin (2012) has done. This approach does not require any distributional assumptions
589
about efficiency; because no stochastic specification is imposed, all variation between production
590
units is interpreted as inefficiency (Hjalmarsson et al., 1996). SFA can overcome this shortcom-
591
ing. However, defining an appropriate and reasonable production function, as is necessary in SFA
592
analysis, is difficult. The analytical thinking in Carberry et al. (2013) is very similar to SFA, con-
593
sidering only one input (nitrogen) and one output (grain yield). However, the production frontiers
594
are simulated with the Agricultural Production Systems Simulator, not with data, as in SFA. Thus,
595
it is impossible to determine which approach is better than the other because the true level of ef-
596
ficiency is unknown. In general, all deviations from the frontier are interpreted as inefficiency in
597
DEA, so the SFA approach normally yields lower inefficiency levels (Battese et al., 2000).
AC C
EP
TE D
M AN U
SC
579
598
A translog stochastic frontier function was adopted to estimate the relative eco-efficiency of
599
provincial grain production in this study. The output frontier is the potential grain output, while 31
ACCEPTED MANUSCRIPT
Table 5
Literature Reith
and
Scale
Object
Method
Farm
An agricultural research com-
Ratio method
Guidry (2003)
plex:
RI PT
Summary of previous farming eco-efficiency research. Definition of eco-efficiency
the Model Sustainable
A ratio of product delivered (calories of food energy) relative to re-
Agricultural Complex
sources consumed (calories of elec-
Farm
Farms belonging to the rain-fed
Data envelop-
A ratio between economic value
agricultural system of Campos
ment analysis
added and environmental pressures
County in Palencia, Spanish
(DEA)
National systems of crop and
Input-
The ratio of the smallest total cu-
Alauddin
livestock production in 30 OECD
orientated
mulative exergy amount to the ob-
(2012)
countries from 1990 to 2003
DEA
served cumulative exergy amount
Olive farms in Andalusia
DEA
A ratio between net income and a
et al. (2011)
Hoang
and
G´omez-Lim´on
Country
Farm
M AN U
Picazo-Tadeo
SC
trical or fossil energy)
Carberry et al.
Farm
(2013)
et al. (2014) M¨uller et al.
Comparison of crop yields against
China, Zimbabwe and Australia
production
simulated grain yields at farmer-
frontiers
specified levels of nitrogen input
Ratio method
Total value product per environ-
Paddy rice production in northeastern Thailand
Farm
mental impact
Kiwifruit production in New
Ratio method
Zealand
Beltr´an-Esteve
Farm
Region
Net profit per kg greenhouse gas emissions
Spanish citrus farms
DEA
A ratio between economic value
et al. (2017)
This study
mance
Compare with
AC C
(2015)
Country
measure of environmental perfor-
Diverse cropping systems in
EP
Thanawong
TE D
et al. (2012)
and an aggregate of damaging environmental impacts arising from farms’ economic activity
Grain production in China
The ratio of the actual output to the
Stochastic frontier
ap-
proach (SFA)
32
potential output
ACCEPTED MANUSCRIPT
the input is expressed in terms of five variables: capital, labour, green WF, blue WF and grey WF.
601
The selection of indicators is very different from previous studies mainly in environmental per-
602
formance indicators, although the concept of involving resource consumption and environmental
603
impacts in farming production is common. Some previous studies have focused on the individual
604
environmental performance of farming, such as the electrical or fossil energy consumed (Reith and
605
Guidry, 2003), cumulative exergy amount (Hoang and Alauddin, 2012), nitrogen (Carberry et al.,
606
2013), and greenhouse gas emissions (M¨uller et al., 2015). Others have used a comprehensive
607
environmental indicator (Picazo-Tadeo et al., 2011; Thanawong et al., 2014). The eco-efficiency
608
assessment comprises comprehensive efficiency of economics, resources and the environment in
609
this paper. It is achieved by combining traditional production factor inputs (capital and labour)
610
with natural water resources (green water), irrigation water resources (blue water) and the envi-
611
ronmental impact of the overuse of fertilizers, pesticides, etc. (grey water).
M AN U
SC
RI PT
600
Furthermore, the path for moving closer to the efficiency frontier is analysed in this paper by an
613
influencing factors analysis. Farmer feature and farm feature variables have been used to analyse
614
the determinants of eco-efficiency using truncated regression and bootstrapping techniques, and
615
the results show that farmers who benefit from agri-environmental programs as well as those with
616
a university-level education were found to be more eco-efficient (Picazo-Tadeo et al., 2011). The
617
result in G´omez-Lim´on et al. (2012) is that soil-climate conditions strongly influence managerial
618
eco-efficiency. Unlike previous studies, this study investigated the determinants of eco-efficiencies
619
at a regional level; hence, some macro variables were selected as possible factors. The results
620
show that the per capita GDP, per capita water supply, proportion of government expenditure
621
on environmental protection and proportion of non-disaster areas positively influenced the grain
622
production eco-efficiency.
623
5.2. Output elasticity of inputs
AC C
EP
TE D
612
624
The output elasticity analysis of individual inputs can address key factors for improving eco-
625
efficiency. It is implementable with the SFA technique. Based on Eq. 8, the elasticity of output
33
ACCEPTED MANUSCRIPT
626
with respect to the jth input ( j ) is calculated as j =
1X ∂ ln y = βj + β jk ln xk + β j j ln x j . ∂ ln x j 2 j,k
(10)
With Eq. 10 and estimate results of grain production SFA model shown in Table 3, the output
628
elasticities of the five input variables over time in China are calculated. The results are presented
629
in Fig. 9. The returns to scale (sum of all output elasticities of inputs) were estimated to be greater
630
than one for all years, which indicates that there were increasing returns to scale; increasing the
631
inputs by 1% would produce a more than 1% increase in output.
0.75
SC
Grey water footprint
AC C
0.00
Blue water footprint
TE D
0.50
0.25
Green water footprint
M AN U
Labor inout
EP
The elasticity of output with respect to the inputs
Capital stock
RI PT
627
1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015
Fig. 9. The elasticities of output with respect to the inputs.
632
Regarding individual input, the output elasticities of capital were the highest, and they in-
633
creased continuously over the study period. However, the output elasticities of labour tended
634
to decrease over time. These results imply that mechanization was the driving force behind the
635
grain production growth in China. The output elasticity of the green WF was greater than zero 34
ACCEPTED MANUSCRIPT
and increased steadily. However, the output elasticity of the blue WF was estimated as negative
637
throughout the study period. These results reflect the fact that China’s grain production pattern
638
runs counter to the distribution patterns of water-heat conditions (Li et al., 2017). The grain pro-
639
duction barycenter in China has been moving northwards continuously over time (Wang et al.,
640
2018). The conditions of water and temperature in the northern region are worse than those in
641
the southern region. This situation explains why increasing blue WF cannot increase overall grain
642
output in China. Another reason is the differences in crop structure among regions. Maize, which
643
has a lower output value than other grains, plays a dominant role in North China’s grain output
644
(Wang et al., 2018). The output elasticity of the grey WF decreased over the study period and
645
has decreased below zero in recent years. These observations imply that increasing the amount
646
of chemical fertilizer cannot increase grain output; rather, it will decrease grain output. Chemical
647
fertilizers continue to be overused in China, and this overuse has resulted in a series of environ-
648
mental consequences (Jiao et al., 2018). The main reason for this overuse is a lack of awareness
649
of nutrient management and environmental protection. The existence of negative output elastici-
650
ties of the blue and grey WFs implies that improving irrigation water productivity and controlling
651
environmental cost are urgent for grain output growth. The output elasticities of the three WFs
652
according to the different provinces are calculated for addressing the key regions (Fig. 10).
TE D
M AN U
SC
RI PT
636
As shown in Fig. 10, the annual average output elasticity of the green WF was positive in
654
all provinces. The three provinces with the largest output elasticities of the green WF were Tibet
655
(0.270), Qinghai (0.258) and Heilongjiang (0.197). The annual average output elasticities of the
656
blue WF were negative in some provinces. Controlling water logging and adjusting crop con-
657
struction are necessary for those provinces, which include Gansu (-0.142), Guangxi (-0.128) and
658
Jiangxi (-0.126). In provinces with negative output elasticities of the grey WF, such as Henan
659
(-0.163), Jiangsu (-0.137) and Anhui (-0.131), more fertilizer input will produce less grain. These
660
provinces are the targets of environmental conservation with respect to grain production.
661
5.3. Changes in eco-efficiency and total factor productivity
AC C
EP
653
662
There was some confusion regarding the technical efficiency and total factor productivity. In
663
this section, the change in eco-efficiency and the relationship between eco-efficiency and total 35
0.2
0.1
0.0
-0.1
EP TE D
0.3
AC C
-0.15 G an G su ua n Ji gxi an G gxi u H izh ei o lo u n Q gjia in ng g Y h In un ai ne n r_ an M Si ong ch o u ri H an a un Sh an an Ti xi C be ho t ng N qin in g gx An ia hu Ji i l Xi in n Sh jian a g G nd ua on ng g d H on eb g H ei en H an ai n Fu an jia H n ub Sh ei a Li anx ao i Zh nin ej g i Ji ang an Sh gs an u g Ti ha an i Be jin ijin g
Elasticity of blue water footprint 0.00
-0.05
-0.10
M AN U
0.05
SC
Elasticity of green water footprint 0.1
RI PT
an g H su en Sh an aa H nxi eb Be ei ijin H g ub Sh e an i d Ti ong an Xi jin nj ia Fu ng G jia ua n ng Si do ch ng Li uan ao Zh nin ej g ia Sh ng a C nx ho i ng q Ji ing lin N in gx An ia h H ui un Sh an an Yu gha In ne nn i r_ an M o H ng ai o na ria G n an G su ua G ngx ui i zh Ji ou a H ng ei x lo i ng Q jia in ng gh Ti ai be t
Ji
0.0
H en Ji an an g An su hu Be i ijin H g ub Li ei ao Sh nin an g do Ji ng lin Ti an Sh jin aa H nxi e Zh bei e G jia ua ng H ngd ei lo on n g Sh gjia an ng C gh ho a ng i H qin un g a Fu n jia Si n ch Yu uan nn H an ai n G a In uiz n ne h r_ ou M o Sh ng an ori Ji xi a an G gxi ua n Xi gx nj i ia N ng in g G xia an s Ti u be Q t in gh ai
Elasticity of grey water footprint
ACCEPTED MANUSCRIPT
0.2
Fig. 10. The provincial output elasticities of water footprints.
36
ACCEPTED MANUSCRIPT
664
factor productivity are discussed. According to Kumbhakar and Lovell (2000) and Kim and Han
665
(2001), the logarithm of y in Eq. 8 is completely differentiated with respect to time to obtain
∂ ln y ∂t
(11)
RI PT
d ln y ∂ ln y X d ln x j du = + − . j dt ∂t dt dt j
j
d ln x j dt
666
Where d dtln y measures the output change rate;
667
measures the change rate of ecological inefficiency. measures the change rate of input use; and − du dt
668
The change rate for total factor productivity is defined as the output change rate, which is unexplained by the input change rate:
M AN U
d ln x j d ln y X d ln x j ∂ ln y du X − Sj = − + j − S j dt dt ∂t dt dt j j ! X j d ln x j X j d ln x j ∂ ln y du X P P −Sj − + j − 1 + = ∂t dt dt dt j j j j j j j
T FPC =
P j
SC
669
measures the rates of technical progress;
(12)
= T P + EC + S C + AEC.
672
673
674
TE D
671
Where T FPC is the change rate of total factor productivity; S j is input j’s proportion of proP duction costs; j represents the input elasticities defined at the production frontier; j j denotes the measurement of returns to scale; T P is the rates of technical progress; EC is the change P j d ln x j P rate of ecological inefficiency; S C = represents scale components; and j P j j dt j j − 1 P j d ln x AEC = j P j j − S j dt j represents the allocative efficiency change rate, which measures in-
EP
670
efficiency in resource allocation resulting from deviations of input prices from the value of their
676
marginal product. Thus, in Eq. 12, the change rate for total factor productivity can be decomposed
677
into rates of technical progress, the eco-efficiency change rate, scale components and the allocative
678
efficiency change rate.
679
AC C
675
The technology progress is calculated as 5
TP =
∂ ln y 1X = βt + β jt ln x j + βtt t. ∂t 2 j=1
37
(13)
ACCEPTED MANUSCRIPT
The eco-efficiency change rate is calculated as EC =
681
Et − Et−1 . Et
(14)
The change rate of inputs is calculated as d ln x j ln x jt − ln x j(t−1) = . dt ln x j(t−1)
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(15)
The components of the change rate for total factor productivity described in Eq. 12 are then
683
calculated for each region over time based on the results in Table 3. The allocative efficiency was
684
not calculated because the cost of virtual water use was not available. Fig. 11 presents the aver-
685
ages of the rates of technical progress, the eco-efficiency change rate, the scale components and
686
the total factor productivity growth. Technical progress and eco-efficiency were the most impor-
687
tant aspects of total factor productivity. The annual average total factor productivity growth rate
688
was estimated as 0.034 over the period 1997-2008, with 40.64% attributed to technical progress
689
(0.014), 60.20% to eco-efficiency (0.021), and only -0.84% to scale components (-0.0003). The
690
rates of technical progress became positive in 2003 and increased continuously during the study
691
period. Eco-efficiency change was the key driver of total factor productivity, and it consistently
692
had the same direction.
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The total factor productivity
Technology progress
Eco-efficiency
Scale Composition
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0.2
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Change rate
0.1
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0.0
-0.1
1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015
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Fig. 11. The decomposition of total factor productivity.
6. Conclusions and implications
The eco-efficiency is a major access to achieve sustainability. Eco-efficiency assessment can
695
provide policymakers with reliable information to design environmentally sustainable managerial
696
strategies and policies. The present study developed an integrated WF-SFA method by combining
697
WF estimation and the SFA model to estimate the eco-efficiency of grain production in China.
698
First, the green, blue and grey WFs, representing natural water resources, irrigation water re-
699
sources and environmental impact of grain production receptivity, respectively, were calculated
700
by a WF analysis. Then, by identifying the three WFs plus capital and labour as inputs and grain
701
output value as the only output, a stochastic frontier function of grain production was established
702
to estimate eco-efficiency. Eco-efficiency was defined as the ratio of the actual output to the po-
703
tential output, and it was estimated with the SFA technique. The eco-inefficiency effects were also
704
modelled using six influencing factors.
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The main findings are summarized as follows. First, the average annual grain production WF
706
in China from 1997 to 2015 was 820.37 billion m3 . Specifically, 36.36% was green WF, 26.66%
707
was blue WF, and 36.98% was grey WF. Second, the eco-efficiencies were estimated to be within
708
the range of 0.424-0.986, with an average value of 0.807. There is potential for China to make its
709
grain production system more environmentally and ecologically sustainable. Third, the per capita
710
GDP, per capita water supply, proportion of government expenditure on environmental protection
711
and proportion of disaster areas are key factors affecting eco-efficiency at the regional level. In
712
addition, the output elasticity analysis showed that the blue and grey WFs have negative output
713
elasticities in recent years. They are key inputs that need to be controlled for eco-efficiency im-
714
provement. China must feed its large population with minimizing the environmental impact while
715
helping to sustainable development goals as well. These findings can help China design relevant
716
policies of agricultural sustainability focused on crop distribution, efficient irrigation water use
717
and nutrient and pollutant management.
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This study provides a new method for the eco-efficiency assessment of grain production that
719
could potentially be applied in other fields. It also faced some limitations. One weakness of SFA
720
is that if the functional form is specified incorrectly, then the measured efficiency may be con-
721
founded with the specification errors. Although the WF-SFA framework used in this study has
722
been proven suitable for eco-efficiency assessments of grain production, transferring it to other
723
fields may involve huge challenges. Moreover, the resource consumption and environmental im-
724
pact related to grain production are very complicated and can be difficult to quantify. This study
725
considers a small number resources and environmental indicators. Future studies should address
726
these limitations by improving SFA and by taking into account additional environmental impacts
727
of production systems.
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Acknowledgments
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This work is supported by the National Natural Science Foundation of China (71503202,
730
71573208), the Humanities and Social Sciences Foundation of the Ministry of Education of China
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(17YJC790126) and the Science Foundation of Shaanxi Province of China (2016JQ7001). 40
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References
733
Aigner, D., Lovell, C., Schmidt, P., 1977. Formulation and estimation of stochastic frontier production function
735 736
models. J. Econom. 6 (1), 21–37. Alves, J. L. S., Dumke De Medeiros, D., 2015. Eco-efficiency in micro-enterprises and small firms: A case study in the automotive services sector. J. Clean. Prod. 108, 595–602.
RI PT
734
737
Aoe, T., 2007. Eco-efficiency and ecodesign in electrical and electronic products. J. Clean. Prod. 15 (15), 1406–1414.
738
Basset-Mens, C., Ledgard, S., Boyes, M., 2009. Eco-efficiency of intensification scenarios for milk production in New
742 743 744 745 746 747 748 749 750 751
to paddy farmers in India. J. Prod. Anal. 3 (1-2), 153–169.
SC
741
Battese, G. E., Coelli, T. J., 1992. Frontier production functions, technical efficiency and panel data: With application
Battese, G. E., Coelli, T. J., 1995. A model for technical inefficiency effects in a stochastic frontier production function for panel data. Empir. Econ. 20 (2), 325–332.
M AN U
740
Zealand. Ecol. Econ. 68 (6), 1615–1625.
Battese, G. E., Heshmati, A., Hjalmarsson, L., 2000. Efficiency of labour use in the Swedish banking industry: A stochastic frontier approach. Empir. Econ. 25 (4), 623–640.
Beltr´an-Esteve, M., G´omez-Lim´on, J. A., Picazo-Tadeo, A. J., Reig-Mart´ınez, E., 2014. A metafrontier directional distance function approach to assessing eco-efficiency. J. Prod. Anal. 41 (1), 69–83. Beltr´an-Esteve, M., Reig-Mart´ınez, E., Estruch-Guitart, V., 2017. Assessing eco-efficiency: A metafrontier directional distance function approach using life cycle analysis. Environ. Impact Asses. 63, 116–127.
TE D
739
Bogetoft, P., Otto, L., 2011. Benchmarking with DEA, SFA and R. International Series in Operations Research & Management Science. Springer, New York.
Caiado, R. G. G., de Freitas Dias, R., Mattos, L. V., Quelhas, O. L. G., Leal Filho, W., 2017. Towards sustainable
753
development through the perspective of eco-efficiency - A systematic literature review. J. Clean. Prod. 165, 890–
754
904.
756
Camarero, M., Castillo, J., Picazo-Tadeo, A. J., Tamarit, C., 2013. Eco-Efficiency and Convergence in OECD Countries. Environ. Resour. Econ. 55 (1), 87–106.
AC C
755
EP
752
757
Carberry, P. S., Liang, W.-l., Twomlow, S., Holzworth, D. P., Dimes, J. P., McClelland, T., Huth, N. I., Chen, F.,
758
Hochman, Z., Keating, B. A., 2013. Scope for improved eco-efficiency varies among diverse cropping systems. P.
759
Natl. Acad. Sci. USA 110 (21), 8381–8386.
760
Cerutti, A. K., Beccaro, G. L., Bagliani, M., Donno, D., Bounous, G., 2013. Multifunctional Ecological Footprint
761
Analysis for assessing eco-efficiency: A case study of fruit production systems in Northern Italy. J. Clean. Prod.
762
40, 108–117.
763 764
Cha, K., Lim, S., Hur, T., 2008. Eco-efficiency approach for global warming in the context of Kyoto Mechanism. Ecol. Econ. 67 (2), 274–280.
41
ACCEPTED MANUSCRIPT
766 767 768 769 770
Charmondusit, K., Keartpakpraek, K., 2011. Eco-efficiency evaluation of the petroleum and petrochemical group in the map Ta Phut Industrial Estate, Thailand. J. Clean. Prod. 19 (2-3), 241–252. Charmondusit, K., Phatarachaisakul, S., Prasertpong, P., 2014. The quantitative eco-efficiency measurement for small and medium enterprise: A case study of wooden toy industry. Clean Technol. Envir. 16 (5), 935–945. China Meteorological Data Service Center, 1997-2015. Meteorological Data Set. Available at: http://data.cma.
RI PT
765
cn/data/cdcindex/cid/6d1b5efbdcbf9a58.html. (access on 6/22/2016).
771
Chinese Ministry of Agriculture, 1998-2016. China Agriculture Yearbook. China Agriculture Press, Beijing.
772
Chinese National Bureau of Statistics, 1998-2016. China Statistical Yearbook. China Statistics Press, Beijing.
773
Chinese National Bureau of Statistics, Ministry of Environmental Protection, 1998-2016. China Statistical Yearbook
776 777 778 779 780
SC
775
on the Environment. China Statistics Press, Beijing.
Coelli, T. J., 1995. Recent developments in frontier modelling and efficiency measurement. Aust. J. Agr. Ecno. 39 (3), 219–245.
M AN U
774
Coelli, T. J., 1996. A Guide to Frontier Version 4.1: A Computer Program for Stochastic Frontier Production and Cost Function Estimation. CEPA Working Papers WP7/96, University of Queensland. 1–13. Coelli, T. J., Rao, D. P., O’Donnell, C. J., Battese, G. E., 2005. An Introduction to Efficiency and Productivity Analysis, 2nd Edition. Springer, New York.
781
Doll, P., Siebert, S., 2002. Global modeling of irrigation water requirements. Water Resour. Res. 38 (4), 1–10.
782
Egilmez, G., Park, Y. S., 2014. Transportation related carbon, energy and water footprint analysis of U.S. manufac-
786 787 788 789 790 791
TE D
785
Farrell, M. J., 1957. The Measurement of Productive Efficiency. J. Royal Stat. Society. Series A (General) 120 (3), 253–290.
G´omez-Lim´on, J. A., Picazo-Tadeo, A. J., Reig-Mart´ınez, E., 2012. Eco-efficiency assessment of olive farms in Andalusia. Land Use Policy 29 (2), 395–406.
EP
784
turing: An eco-efficiency assessment. Transport Res. D-Tr. E. 32, 143–159.
Hjalmarsson, L., Kumbhakar, S. C., Heshmati, A., 1996. DEA, DFA and SFA: A comparison. J. Prod. Anal. 7 (2-3), 303–327.
Ho, T. Q., Hoang, V. N., Wilson, C., Nguyen, T. T., 2018. Eco-efficiency analysis of sustainability-certified coffee
AC C
783
production in Vietnam. J. Clean. Prod. 183, 251–260.
792
Hoang, V. N., Alauddin, M., 2012. Input-Orientated Data Envelopment Analysis Framework for Measuring and De-
793
composing Economic, Environmental and Ecological Efficiency: An Application to OECD Agriculture. Environ.
794
Resour. Econ. 51 (3), 431–452.
795
Hoekstra, A. Y., 2013. The Water Footprint of Modern Consumer Society. Routledge, London.
796
Hoekstra, A. Y., Chapagain, A. K., Aldaya, M. M., 2011. The Water Footprint Assessment Manual. Earthscan, Lon-
797 798
don. Huang, J., Yang, X., Cheng, G., Wang, S., 2014. A comprehensive eco-efficiency model and dynamics of regional
42
ACCEPTED MANUSCRIPT
799
eco-efficiency in China. J. Clean. Prod. 67, 228–238.
800
Huppes, G., Ishikawa, M., 2005. A Framework for Quantified Eco-Efficiency Analysis. J. Ind. Ecol. 9 (4), 25–42.
801
Jiao, X. Q., He, G., Cui, Z. L., Shen, J. B., Zhang, F. S., 2018. Agri-environment policy for grain production in China:
805 806 807 808 809 810 811 812
RI PT
804
Jollands, N., Lermit, J., Patterson, M., 2004. Aggregate eco-efficiency indices for New Zealand - A principal components analysis. J. Environ. Manage. 73 (4), 293–305.
Kim, S., Han, G., 2001. A decomposition of total factor productivity growth in Korean manufacturing industries: A stochastic frontier approach. J. Prod. Anal. 16 (3), 269–281.
Kontodimopoulos, N., Papathanasiou, N. D., Flokou, A., Tountas, Y., Niakas, D., 2011. The impact of nondiscretionary factors on DEA and SFA technical efficiency differences. J. of Med. Syst. 35 (5), 981–989.
SC
803
Toward sustainable intensification. China Agr. Econ. Rev. 10 (1), 78–92.
Koskela, M., 2015. Measuring eco-efficiency in the Finnish forest industry using public data. J. Clean. Prod. 98, 316–327.
M AN U
802
Kumbhakar, S. C., Ghosh, S., McGuckin, J. T., 1991. A Generalized Production Frontier Approach for Estimating Determinnatns of Inefficiency in US Dairy Farms. J. Bus. Econ. Stat. 9 (3), 279–286.
813
Kumbhakar, S. C., Lovell, C. a. K., 2000. Stochastic frontier analysis. Cambridge University Press, Cambridge.
814
Kuosmanen, T., Kortelainen, M., 2005. Measuring eco-efficiency of production with data envelopment analysis. J.
818 819 820 821 822 823 824 825 826 827 828 829 830 831 832
sis. J. Life Cycle Ass. 7 (5), 261–268.
TE D
817
Landsiedel, R., Saling, P., 2002. Assessment of toxicological risks for life cycle assessment and eco-efficiency analy-
Lauwers, L., 2009. Justifying the incorporation of the materials balance principle into frontier-based eco-efficiency models. Ecol. Econ. 68 (6), 1605–1614.
Li, T., Long, H., Zhang, Y., Tu, S., Ge, D., Li, Y., Hu, B., 2017. Analysis of the spatial mismatch of grain production and farmland resources in China based on the potential crop rotation system. Land Use Policy 60, 26–36.
EP
816
Ind. Ecol. 9 (4), 59–72.
Li, D., Zhu, J., Hui, E. C. M., Leung, B. Y. P., Li, Q., 2011. An emergy analysis-based methodology for eco-efficiency evaluation of building manufacturing. Ecol. Indic. 11 (5), 1419–1425. Meeusen, W., van Den Broeck, J., 1977. Efficiency Estimation from Cobb-Douglas Production Functions with Com-
AC C
815
posed Error. Int. Econ. Rev. 18 (2), 435. Mekonnen, M. M., Hoekstra, A. Y., 2011. The green, blue and grey water footprint of crops and derived crop products. Hydrol. Earth Syst. Sc. 15 (5), 1577–1600. Michelsen, O., Fet, A. M., Dahlsrud, A., 2006. Eco-efficiency in extended supply chains: A case study of furniture production. J. Environ. Manage. 79 (3), 290–297. Mickwitz, P., Melanen, M., Rosenstr¨om, U., Sepp¨al¨a, J., 2006. Regional eco-efficiency indicators - a participatory approach. J. Clean. Prod. 14 (18), 1603–1611. M¨uller, K., Holmes, A., Deurer, M., Clothier, B. E., 2015. Eco-efficiency as a sustainability measure for kiwifruit
43
ACCEPTED MANUSCRIPT
833
production in New Zealand. J. Clean. Prod. 106, 333–342.
834
Organization for Economic Co-operation and Development (OECD), 1998. Eco-efficiency. Paris.
835
Park, H. S., Behera, S. K., 2014. Methodological aspects of applying eco-efficiency indicators to industrial symbiosis
837 838
networks. J. Clean. Prod. 64, 478–485. Park, P. J., Tahara, K., Inaba, A., 2007. Product quality-based eco-efficiency applied to digital cameras. J. Environ.
RI PT
836
Manage. 83 (2), 158–170.
839
Park, Y. S., Egilmez, G., Kucukvar, M., 2015. A Novel Life Cycle-based Principal Component Analysis Framework
840
for Eco-efficiency Analysis: Case of the United States Manufacturing and Transportation Nexus. J. Clean. Prod.
841
92, 327–342.
846 847 848 849 850 851 852 853 854 855 856 857 858 859 860 861 862 863 864 865 866
SC
M AN U
845
Price Division of Chinese National Development and Reform Commission, 1998-2016. China agricultural products cost-benefit compilation of information. China Price Press, Beijing.
Quariguasi Frota Neto, J., Walther, G., Bloemhof, J., van Nunen, J. A., Spengler, T., 2009. A methodology for assessing eco-efficiency in logistics networks. Eur. J. Oper. Res. 193 (3), 670–682. Rashidi, K., Farzipoor Saen, R., 2015. Measuring eco-efficiency based on green indicators and potentials in energy saving and undesirable output abatement. Energ. Econ. 50, 18–26.
Reifschneider, D., Stevenson, R., 1991. Systematic Departures from the Frontier: A Framework for the Analysis of Firm Inefficiency. Int. Econ. Rev. 32 (3), 715.
TE D
844
opment Analysis approach. J. Environ. Manage. 92 (4), 1154–1164.
Reinhard, S., Knox Lovell, C. A., Thijssen, G. J., 2000. Environmental efficiency with multiple environmentally detrimental variables; estimated with SFA and DEA. Eur. J. Oper. Res. 121 (2), 287–303. Reith, C. C., Guidry, M. J., 2003. Eco-efficiency analysis of an agricultural research complex. J. Environ. Manage. 68 (3), 219–229.
EP
843
Picazo-Tadeo, A. J., G´omez-Lim´on, J. A., Reig-Mart´ınez, E., 2011. Assessing farming eco-efficiency: A Data Envel-
Robaina-Alves, M., Moutinho, V., MacEdo, P., 2015. A new frontier approach to model the eco-efficiency in European countries. J. Clean. Prod. 103, 562–573.
Sailing, P., Kicherer, A., Dittrich-Kr¨aamer, B., Wittlinger, R., Zombik, W., Schmidt, I., Schrott, W., Schmidt, S., 2002.
AC C
842
Eco-efficiency analisis by BASF: the method. Int. J. Life Cycle Ass. 7 (4), 203–218. ¨ Schaltegger, S., Sturm, A., 1990. Okologische rationalit¨at: ansatzpunkte zur ausgestaltung von o¨ kologieorientierten managementinstrumenten. Die Unternehmung 4, 273–290. Sepp¨al¨aa, J., Melanen, M., M¨aenp¨aa¨ , I., Koskela, S., Tenhunen, J., Hiltunen, M.-R., 2005. How Can the Eco-efficiency of a Region be Measured and Monitored? J. Ind. Ecol. 9 (4), 117–130. Sun, S., Wang, Y., Engel, B. A., Wu, P., 2016. Effects of virtual water flow on regional water resources stress: A case study of grain in China. Sci. Total Environ. 550, 871–879. Thanawong, K., Perret, S. R., Basset-Mens, C., 2014. Eco-efficiency of paddy rice production in Northeastern Thai-
44
ACCEPTED MANUSCRIPT
870 871 872 873 874 875 876 877 878
Van Caneghem, J., Block, C., Cramm, P., Mortier, R., Vandecasteele, C., 2010a. Improving eco-efficiency in the steel industry: The ArcelorMittal Gent case. J. Clean. Prod. 18 (8), 807–814. Van Caneghem, J., Block, C., Van Hooste, H., Vandecasteele, C., 2010b. Eco-efficiency trends of the Flemish industry:
RI PT
869
Van Berkel, R., 2007. Eco-efficiency in the Australian minerals processing sector. J. Clean. Prod. 15 (8-9), 772–781.
Decoupling of environmental impact from economic growth. J. Clean. Prod. 18 (14), 1349–1357.
Wang, J., Zhang, Z., Liu, Y., 2018. Spatial shifts in grain production increases in China and implications for food security. Land Use Policy 74 (April 2017), 204–213.
World Business Council for Sustainable Development (WBCSD), 1992. Eco-efficiency Learning Module. Available at: www.wbcsd.org (access on 6/22/2016).
SC
868
land: A comparison of rain-fed and irrigated cropping systems. J. Clean. Prod. 73, 204–217.
Wursthorn, S., Poganietz, W. R., Schebek, L., 2011. Economic-environmental monitoring indicators for European countries: A disaggregated sector-based approach for monitoring eco-efficiency. Ecol. Econ. 70 (3), 487–496.
M AN U
867
879
Yang, L., Zhang, X., 2018. Assessing regional eco-efficiency from the perspective of resource, environmental and
880
economic performance in China: A bootstrapping approach in global data envelopment analysis. J. Clean. Prod.
881
173, 100–111.
885 886 887 888 889 890 891 892 893
Zhang, B., Bi, J., Fan, Z., Yuan, Z., Ge, J., 2008. Eco-efficiency analysis of industrial system in China: A data envelopment analysis approach. Ecol. Econ. 68 (1-2), 306–316.
TE D
884
pressure from economic growth. Ecol. Indic. 24, 177–184.
Zhang, J., Liu, Y., Chang, Y., Zhang, L., 2017. Industrial eco-efficiency in China: A provincial quantification using three-stage data envelopment analysis. J. Clean. Prod. 143, 238–249. Zhang, Y., Li, Y., Ouyang, Z., Liu, J., 2015. The grey water footprint of the winter wheat-summer maize crop rotation system of the North China Plain. Acta Ecologica Sinica (in Chinese) 35 (20), 6647–6654.
EP
883
Yu, Y., Chen, D., Zhu, B., Hu, S., 2013. Eco-efficiency trends in China, 1978-2010: Decoupling environmental
Zhou, P., Ang, B. W., Zhou, D. Q., 2012. Measuring economy-wide energy efficiency performance: A parametric frontier approach. Appl. Energ. 90 (1), 196–200. Zhu, Z., Wang, K., Zhang, B., 2014. Applying a network data envelopment analysis model to quantify the eco-
AC C
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efficiency of products: A case study of pesticides. J. Clean. Prod. 69, 67–73.
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An integrated WF-SFA method is developed to estimate eco-efficiency and its
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determinants. Provincial water footprint and grain production eco-efficiency in China are
assessed.
Determinants of eco-efficiency and key inputs of ecological grain production are
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analysed.
The change in eco-efficiency and its role in the total factor productivity are
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S0959-6526(19)32535-1
DOI:
https://doi.org/10.1016/j.jclepro.2019.117685
Article Number: 117685 Reference:
JCLP 117685
To appear in:
Journal of Cleaner Production
Received Date: 28 December 2018 Revised Date:
10 July 2019
Accepted Date: 15 July 2019
Please cite this article as: Song J, Chen X, Eco-efficiency of grain production in China based on water footprints: A stochastic frontier approach, Journal of Cleaner Production (2019), doi: https:// doi.org/10.1016/j.jclepro.2019.117685. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
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Eco-efficiency of grain production in China based on water footprints: a stochastic frontier approach
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Jianfeng Songa , Xiaonan Chena,∗
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College of Economics and Management, Northwest A&F University, Yangling, Shaanxi 712100, PR China
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Abstract
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Eco-efficiency has consistently been of interest to researchers and policy makers. Many methods
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have been employed to calculate eco-efficiency, with the exception of the stochastic frontier ap-
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proach, which is popular in efficiency and productivity analysis. Considering the strengths of the
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stochastic frontier approach and the features of grain production, an integrated WF-SFA method
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combining water footprint assessment and the stochastic frontier approach is proposed in this
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work. In the method, the green, blue and grey water footprints of grain production are calculated.
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Then, a translog stochastic frontier production function with actual grain output value as the only
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output and capital, labour and water footprints as the inputs is established. Next, eco-efficiency,
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which is defined as the ratio of the actual output to the potential output, can be assessed. This
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method is developed here to analyse the eco-efficiency of grain production and its determinants in
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China. The main empirical results are as follows. 1) The annual average grain production water
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footprint in China from 1997 to 2015 was 820.37 billion m3 . 2) The eco-efficiencies were esti-
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mated to be within the range of 0.424-0.986, with an average value of 0.807. There is potential
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for China to increase the environmental and ecological sustainability with its grain production
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system. 3) The per capita GDP, per capita water supply, proportion of government expenditure
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on environmental protection and proportion of non-disaster areas positively influenced the grain
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production eco-efficiency. In addition, the calculated output elasticities of the blue and grey water
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footprints of recent years were negative. These findings can help China design relevant policies
24
of agricultural sustainability focused on crop distribution, efficient irrigation water use and nutri-
25
ent and pollutant management. This research provides a basic framework for the eco-efficiency
26
evaluation of grain production with the stochastic frontier approach which can inform policy and
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strategic development.
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Keywords:
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eco-efficiency, grain production, water footprint, stochastic frontier approach
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1. Introduction
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Grain production requires large amounts of resources (water, land, energy, and chemicals) and
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contributes to environmental pollution, especially water and soil pollution, due to the application of
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fertilizer, pesticides and insecticides and the consequent losses from the system (Thanawong et al.,
34
2014). Increasing the production of major food crops to keep pace with projected increases in food
35
demand while also saving national resources and protecting the environment is an international
36
challenge, given the concerns over growing populations (Carberry et al., 2013). For policymaking,
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it is necessary to have indicators in this context, that is, indicators of economic and environmental
38
efficiency, that compare the evolution of countries or sectors, set goals and implement effective
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policies, either globally or locally (Robaina-Alves et al., 2015).
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Economic efficiency reflects the ability of a production unit to obtain maximal economic output
41
from a given set of production factor inputs (labour, capital, etc.) and the production technology.
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However, it does not imply resource and environmental efficiency (Yang and Zhang, 2018). To
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evaluate whether producers are making efficient use of resources and minimizing environmental
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impacts while achieving their economic objectives, economic-ecological efficiency, known as eco-
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efficiency, may be a useful operational concept (Thanawong et al., 2014).
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Eco-efficiency was first proposed as an instrument for sustainability analysis by Schaltegger
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and Sturm (1990) , and it was subsequently popularized by the World Business Council for
48
Sustainable Development (WBCSD, 1992). According to the Organization for Economic Co-
49
operation and Development (OECD, 1998), eco-efficiency is defined as “the efficiency with which
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ecological resources are used to meet human needs”, and it is calculated as the “product or service
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Corresponding author. Email-address: [email protected]. Telephone number: +86-29-87081140. Fax number: +86-29-87081140. Email addresses: [email protected] (Jianfeng Song), [email protected] (Xiaonan Chen) ∗
Preprint submitted to Journal of Cleaner Production
July 17, 2019
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value divided by environmental influence”. This definition has been adopted widely for illustrat-
52
ing and estimating eco-efficiency. Although eco-efficiency has been further shaped and developed
53
by many other studies, eco-efficiency generally reflects the ability to produce more goods and ser-
54
vices while consuming fewer natural resources and producing less of an impact on the environment
55
(Robaina-Alves et al., 2015).
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Eco-efficiency can be applied to different sectors, such as industrial processes, businesses or
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even to a specific product, and it can also be applied at a regional or global level (Caiado et al.,
58
2017). Previous literature has presented two main general approaches to measure eco-efficiency.
59
One is the ratio method as proposed by the OECD (1998), and the other is a frontier-based ap-
60
proach, data envelopment analysis (DEA). Both approaches address the relative performance com-
61
parison. A more detailed literature about eco-efficiency estimation is presented in Section 2.
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In 1996, the Badische Anilin und Soda Fabrik Corporation (BASF) developed the eco-efficiency ratio methodology which assesses both the economic and environmental impacts of chemicals,
64
processes and products in their lifecycle (Landsiedel and Saling, 2002). After that, calculating
65
the ratio of economic performance to environmental performance based on life cycle assessment
66
has long been identified as the standard method of eco-efficiency analysis. The most common
67
indicator of economic performance is profit, defined as the differences between revenues and
68
costs (Hoang and Alauddin, 2012). Environmental impacts for eco-efficiency calculations usu-
69
ally include energy use, resource use, water use, greenhouse gas emissions and ozone-depleting
70
emissions (M¨uller et al., 2015). The ratio method is very straightforward and communicative, but
71
when more than one economic or environmental indicator is concerned, aggregation into a single
72
numerator or denominator requires appropriate methods and assumptions on aggregation weights
73
(Coelli et al., 2005).
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Farrell (1957) expanded the measurement of efficiency and productivity to production with
75
multiple inputs and/or outputs based on production frontiers. The basis for this measure is the
76
radial contraction/expansion connecting inefficient observed points with (unobserved) reference
77
points on the production frontier. If a decision-making units actual production point lies on the
78
frontier, it is perfectly efficient. If the production point lies below the frontier, it is inefficient,
79
and the ratio of the actual to potential production defines the level of the efficiency of the indi3
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vidual decision-making unit (Coelli et al., 2005). The estimation of efficiency can be categorized
81
according to the assumptions and techniques used to construct the efficient frontier. On the one
82
hand, parametric methods, such as DEA, estimate the frontier with statistical methods. On the
83
other hand, nonparametric methods, such as the stochastic frontier approach (SFA), rely on linear
84
programming to calculate piecewise linear segments of the efficient frontier (Coelli et al., 2005).
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DEA uses linear programming to construct a nonparametric piecewise linear production fron-
86
tier using different return to scales and the possibility of multiple inputs and multiple outputs
87
(Robaina-Alves et al., 2015).Kuosmanen and Kortelainen (2005) first introduced the DEA tech-
88
nique to eco-efficiency analyses. Subsequently, many researchers have developed various DEA
89
models to measure eco-efficiency by incorporating resource inputs and environmental output into
90
the traditional input-output framework of productivity analysis (Yang and Zhang, 2018). DEA has
91
an advantage because it is able to manage complex production environments with multiple inputs
92
and outputs. The advantage over traditional single input single output measures becomes apparent
93
when more than one environmentally detrimental input is involved (Reinhard et al., 2000). To date,
94
DEA has been the most popular method used to measure eco-efficiency from a more aggregated
95
perspective.
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Despite its strengths, DEA does not consider statistical noise, and all deviations from the pro-
97
duction frontier are estimated as technical inefficiency, rendering DEA very sensitive to outliers
98
(Robaina-Alves et al., 2015). As a deterministic method, DEA suffers from measurement errors
99
in the included variables and the omission of unobserved and potentially relevant variables (Kon-
100
todimopoulos et al., 2011). Not controlling for external influences may lead to erroneous DEA
101
efficiency measurements, which, in turn, may provoke uninformed policymaking decisions (Kon-
102
todimopoulos et al., 2011). A parametric stochastic frontier approach developed independently
103
by Aigner et al. (1977) and Meeusen and van Den Broeck (1977) can overcome this shortcoming
104
and has been applied to benchmark efficiency in many fields (Zhou et al., 2012). SFA assumes
105
that the distance of a production unit from the best practice frontier is the sum of the following
106
two components: “true” inefficiency and random fluctuations (Kontodimopoulos et al., 2011). In
107
addition, the other advantages of SFA include the possibility of specification in the case of panel
108
data, formal statistical testing of hypotheses and the construction of confidence intervals Reinhard
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et al. (2000). Despite its strengths, few studies analyse and evaluate eco-efficiency using SFA. The
110
possible reasons are that the functional form used in SFA needs to be correctly specified and that
111
SFA can only be used to estimate the efficiency of production with only one output as noted by
112
Robaina-Alves et al. (2015).
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In the choice between DEA and SFA, a key question is whether one wants flexibility in the
114
mean structure or precision in the noise separation (Bogetoft and Otto, 2011). Coelli (1995) con-
115
cludes that the SFA is recommended for use in agricultural applications, which is the intention of
116
this paper, because measurement error, missing variables, weather, etc. are likely to play a sig-
117
nificant role in agriculture. Considering the advantages and restrictions of the SFA technique, an
118
integrated WF-SFA methodology that combines a water footprint (WF) analysis within the SFA
119
framework is proposed for the estimation of the eco-efficiency of Chinese grain production in this
120
paper. In the first phase, a WF analysis is employed to quantify the resource consumption and en-
121
vironmental impacts associated with grain production, and in the second phase, an SFA model in
122
which the grain output value represents the only output and capital, labour and water footprints are
123
chosen to represent the inputs is adopted to benchmark the relative performance of eco-efficiency.
124
The WF is an indicator of the direct and indirect appropriation of freshwater resources with pro-
125
duction or consumption (Hoekstra et al., 2011). The total WF includes green, blue and grey WFs.
126
The green WF refers to the consumption of rainwater, the blue WF refers to the consumption of
127
surface and groundwater, and the grey WF refers to pollution and is defined as the volume of
128
freshwater required to assimilate a load of pollutants given the natural background concentrations
129
and existing ambient water quality standards (Mekonnen and Hoekstra, 2011). A few studies have
130
applied WF to eco-efficiency analyses without using the SFA technique. Egilmez and Park (2014)
131
quantified the transportation-related carbon, energy and WF of a nation’s manufacturing sectors
132
and evaluated the environmental and economic performance based on eco-efficiency scores. In
133
contrast to previous studies, the three WFs representing resource consumption and environmental
134
impact are considered inputs of the production function in this study. This study not only includes
135
most externalities in grain production but also offers possibilities to measure eco-efficiency with
136
the SFA technique. In the WF-SFA model, similar to studies using output-oriented DEA, eco-
137
efficiency is defined as the ratio of the actual output to the frontier output (technical efficiency of
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ecological production). A more detailed description of the method is provided in Section 3. Compared to previous methods used for eco-efficiency estimation, the WF-SFA method has
140
the following advantages. (1) The capital, labour and water footprints are selected to represent
141
the inputs of agricultural production. Therefore, the estimated eco-efficiency indicator provides
142
the overall outcome of the economic, resource and environmental efficiency of the joint use of
143
all production factors. (2) Using this method, the production frontier is considered by technical
144
inefficiency, measurement error, statistical noise and other non-systematic influences, avoiding
145
the possibility that a large amount of random noise is potentially mistaken for inefficiency as in
146
DEA. (3) This method can also explain the variations in the eco-inefficiency effects in terms of
147
other variables in a single-stage approach. A determinant analysis of eco-efficiency can also be
148
performed with the DEA technique but in a two-stage process. (4) The output elasticity of each
149
factor (capital, labour and water footprints) can be calculated using the estimated parameters of
150
the frontier of production to reveal the factor responsible for the good or bad performance of the
151
region in terms of eco-efficiency.
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In this paper, we aimed to combine WF and SFA to assess the eco-efficiency of grain pro-
153
duction in China. This aim is approached from three perspectives. (1) An integrated WF and
154
SFA methodology is proposed to estimate eco-efficiency. (2) With this method, the regional eco-
155
efficiency of grain production is estimated to illustrate the combination of food security and envi-
156
ronmental sustainability in China. (3) Furthermore, the determinants of eco-efficiency are studied
157
as well to address the key factors underlying eco-efficiency of grain production in China. In addi-
158
tion, the output elasticities of inputs and the contribution of changes of eco-efficiency to the total
159
factor productivity for grain production in China are discussed. The contribution of this study is
160
twofold, namely, methodological and policy-oriented. On one hand, the WF-SFA method used in
161
this study is a useful attempt in method extend for measuring eco-efficiency and can be used in
162
other fields directly or after adjustment. On the other hand, the results of the empirical analysis
163
can provide detailed information about resource use and technical impact in grain production and
164
can be used to derive policy implications for agricultural sustainability in China.
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The rest of this paper is organized as follows. The next section briefly reviews the main meth-
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ods used for calculating eco-efficiency. Section 3 introduces the methodologies, including the 6
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method for assessing the WF and a grain production SFA model by introducing green, blue and
168
grey WF inputs for estimating eco-efficiency. The regional panel data used in the empirical study
169
is presented as well. Section 4 and Section 5 present the main results and discussions. Section 6
170
summarizes the conclusions and implications.
171
2. Literature review of eco-efficiency estimation
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There are two main general approaches to measuring eco-efficiency. The first involves the
173
development of a ratio indicator of the economic performance per unit of environmental influence,
174
and the second is based on the production frontiers to derive efficiency measures. Both approaches
175
address the relative performance comparison (Hoang and Alauddin, 2012).
176
2.1. The ratio method
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Eco-efficiency is commonly defined and measured as the ratio of the economic performance to
178
the environmental influence (OECD, 1998). Eco-efficiency improves when negative environmental
179
impacts decrease while the value of production is maintained or increased (G´omez-Lim´on et al.,
180
2012). The higher the eco-efficiency indicator value, the higher the product or service value per
181
unit of environmental burden (M¨uller et al., 2015).
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Assessing eco-efficiency requires indicators of both economic and environmental performances
183
(Thanawong et al., 2014). The approach to situating proper economic performance and environ-
184
mental influence indicators has developed and changed in accordance with the considered per-
185
spective and the field of study. In 1996, BASF developed an eco-efficiency analysis approach
186
to assess both the economic and environmental impacts for comparing different alternatives of a
187
defined customer benefit over the whole life cycle (Sailing et al., 2002). It is based on life cycle
188
assessment, and it provides a helpful tool in different fields of the evaluation of product or process
189
alternatives (Sailing et al., 2002). Landsiedel and Saling (2002) extended this approach by con-
190
sidering the assessment of toxicological risks. For better specifying the details of eco-efficiency,
191
Huppes and Ishikawa (2005) established a framework for quantifying the eco-efficiency analysis
192
with a ratio method.
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The choice of indicators for economic and environmental performance is an empirical ques-
194
tion. There is little agreement on it. Generally, economic performance in an eco-efficiency analysis
195
can be quantified in monetary units as sales or as “value added”, which is sales minus the costs of
196
goods and services (Hoang and Alauddin, 2012). Cost-benefit analysis, based on life cycle costs,
197
are commonly used to measure economic performance (Huppes and Ishikawa, 2005). In addition,
198
the most common indicators of environmental performance are material consumption, energy con-
199
sumption, emissions, toxicity potential and risk potential. These indicators were first used in the
200
BASF methodology and are still used currently (Caiado et al., 2017). Life cycle assessment is
201
well known, and it is the best methodology for assessing the potential environmental impact asso-
202
ciated with a production, process, transport or other activity chain by evaluating the emission and
203
resource consumption (Egilmez and Park, 2014).
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The ratio method has been widely used to calculate eco-efficiency in various fields, such as by
205
product (Aoe, 2007; Park et al., 2007), company (Charmondusit and Keartpakpraek, 2011; Alves
206
and Dumke De Medeiros, 2015), project (Cha et al., 2008), industry (Li et al., 2011; Charmondusit
207
et al., 2014), region (Sepp¨al¨aa et al., 2005) and country (Wursthorn et al., 2011; Yu et al., 2013).
208
Michelsen et al. (2006) presented a methodology for estimating eco-efficiency in extended supply
209
chains based on a case study of furniture production in Norway. A framework for eco-efficiency
210
in minerals processing was illustrated with practical examples from gold, base metals, alumina,
211
aluminium and pigment operations in Australia (Van Berkel, 2007). Specific to farming, Reith
212
and Guidry (2003) applied the lessons of industry eco-efficiency analysis to the agricultural sector
213
by measuring the eco-efficiency of Complex in the Model Sustainable Agricultural Complex. The
214
eco-efficiency of a New Zealand dairy farm in terms of milk production and land use was compared
215
using life cycle assessment methodology (Basset-Mens et al., 2009). By defining eco-efficiency
216
on an area basis as the net profit per kg greenhouse gas emissions, M¨uller et al. (2015) estimated
217
eco-efficiency for kiwi fruit production in New Zealand.
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The eco-efficiency ratio indicator is simple to calculate and is useful for supporting policy
219
decisions in a readily intelligible form. However, due to the variety of economic and environ-
220
mental performances, a main challenge in eco-efficiency analysis is how to specify and aggregate
221
economic and environmental effects, mainly environmental effects (Huppes and Ishikawa, 2005). 8
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There have been three main ways to address this issue in empirical research. The predominant
223
approach is to aggregate the relevant environmental effects into a broadly acceptable single-score
224
result based on value judgements or preferences, as most studies have done based on life cycle
225
assessment.
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Another way is calculating individual eco-efficiency indicator for various environmental im-
227
pacts. Van Caneghem et al. (2010b) proposed a methodology for eco-efficiency reporting with
228
eco-efficiency indicators for climate change, acidification, photooxidant formation, human toxic-
229
ity, freshwater aquatic ecotoxicity, eutrophication, energy consumption and waste generation. The
230
eco-efficiency indicators proposed by Park and Behera (2014) included one economic indicator
231
and three generally applicable simplified environmental indicators (raw material consumption, en-
232
ergy consumption and CO2 emissions). Van Caneghem et al. (2010a) illustrated the eco-efficiency
233
of the steel industry with six partial eco-efficiency indicators for the impact categories acidifica-
234
tion, photooxidant formation, human toxicity, freshwater aquatic ecotoxicity, eutrophication and
235
water use. Eco-efficiency indicators were calculated as per impact category (environmental im-
236
pact indicators based upon life cycle assessment and energy and water use analyses) to analyse
237
the eco-efficiency of paddy rice production in north-eastern Thailand (Thanawong et al., 2014). In
238
addition to individual environmental impact indicators, various economic performance indicators
239
could be calculated separately as well. Cerutti et al. (2013) examined sustainable farming in the
240
fruit production systems of the Piemonte Region of Northern Italy based on the quantification of
241
four different ecological footprint applications related to different functional units: tons of product,
242
nutrient content in the fruit produced, hectare of crops and revenue.
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The third way is the optimization of multiple indicators with a participatory approach, Delphi
244
panel or principal components analysis. Mickwitz et al. (2006) proposed regional eco-efficiency
245
indicators using a participatory approach. Koskela (2015) discussed the measurement of eco-
246
efficiency in the Finnish forest industry. The main method used in this research was the Delphi
247
panel, and the eco-efficiency indicators were based on the expert ratings. Principal components
248
analysis is an effective approach for aggregating eco-efficiency indicators and assisting decision
249
makers by reducing redundancy in an eco-efficiency indicators matrix (Jollands et al., 2004). The
250
method has been used to aggregate eco-efficiency indices for New Zealand (Jollands et al., 2004)
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251
and the United States Manufacturing and Transportation Nexus analysis (Park et al., 2015). Overall, when more than one economic or environmental performance indicator is concerned,
253
aggregation into a single numerator or denominator requires appropriate methods and assumptions
254
on aggregation weights (Coelli et al., 2005). The frontier approach can generate objective weights
255
from the data, and it is considered more reasonable in the context of eco-efficiency measurements
256
(Huang et al., 2014).
257
2.2. The frontier-based approach
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Production efficiency models estimate frontier functions and measure the efficiencies of firms
259
relative to the estimated frontiers (Coelli et al., 2005). The concept can be extended to eco-
260
efficiency. There are two main frontier-based approaches: the parametric frontier approach (e.g.
261
SFA) and the nonparametric frontier approach (e.g. DEA). The eco-efficiency frontier approach
262
was first adopted by Kuosmanen and Kortelainen (2005), which assessed the eco-efficiency of road
263
transportation in the three largest towns of eastern Finland using the DEA technique.
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(1) DEA
265
Since Kuosmanen and Kortelainen (2005), a number of studies have investigated eco-efficiency
266
using DEA. Those studies involved various topics, such as the eco-efficiency of industrial systems
267
in China (Zhang et al., 2008), the convergence in eco-efficiency of a group of 22 OECD countries
268
(Camarero et al., 2013), the dynamics of regional eco-efficiency in China (Huang et al., 2014),
269
manufacturing sectors and the transportation industry of the U.S. (Egilmez and Park, 2014) and
270
the eco-efficiencies of ten comparable pesticides (Zhu et al., 2014). Other than the transitional
271
DEA model, with several environmental performance indicators as inputs and one economic per-
272
formance indicator as the output, some more complicated DEA models have been presented re-
273
cently to calculate eco-efficiency. Beltr´an-Esteve et al. (2014) used directional distance functions
274
to extend the nonparametric metafrontier approach to efficiency measurements proposed for the
275
assessment of technological differences in eco-efficiency between groups of producers. Rashidi
276
and Farzipoor Saen (2015) developed a DEA model that divides the inputs into energy and non-
277
energy inputs and the outputs into desirable and undesirable outputs. Zhang et al. (2017) estimated
278
industrial eco-efficiency in China and analysed its determinants using three-stage DEA.
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Many studies assess farming eco-efficiency using DEA as well. Picazo-Tadeo et al. (2011)
280
measured eco-efficiency at the farm level in Spain and studied the determinants of eco-efficiency
281
further using truncated regression and bootstrapping techniques. Hoang and Alauddin (2012) pre-
282
sented an input-oriented DEA framework. With this framework, the economic, environmental and
283
ecological efficiency of OECD agriculture was measured and decomposed. G´omez-Lim´on et al.
284
(2012) used DEA techniques and pressure distance functions to contribute a farm-level assessment
285
of the eco-efficiency of a sample of 292 Andalusian olive farmers. Beltr´an-Esteve et al. (2017)
286
proposed the use of life cycle analysis, a metafrontier directional distance function approach, and
287
DEA to assess technological and managerial differences in eco-efficiency between Spanish citrus
288
farm systems. Besides, an eco-efficiency analysis of sustainability-certified coffee production in
289
Vietnam was conducted by Ho et al. (2018).
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(2) Other frontier-based approaches
291
Compared to DEA, the application of other frontier-based approaches in eco-efficiency mea-
292
surement has had limited discussion. Quariguasi Frota Neto et al. (2009) proposed a methodology
293
based on multi-objective linear programming aimed to handle the visual representation of the eco-
294
efficient frontier. Carberry et al. (2013) diagnosed the state of agricultural production in China,
295
Zimbabwe and Australia. More than 3,000 surveyed crop yields in these three countries were
296
compared against simulated grain yields at farmer-specified levels of nitrogen input (production
297
frontiers).
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Notably, SFA, a mainstream approach in efficiency and productivity analyses, is scarcely used
299
to estimate eco-efficiency. The main reason is that SFA can only be used for estimating the techni-
300
cal efficiency of production with one output indicator. Due to this limitation, Robaina-Alves et al.
301
(2015) presented a new stochastic frontier model to assess technical efficiency that combines in-
302
formation from the DEA and the structure of composed error from the SFA. In addition, Lauwers
303
(2009) considered two different models, environmentally adjusted production efficiency models
304
and frontier eco-efficiency models, and attempted to justify incorporating the materials balance
305
principle into them.
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The strengths and weaknesses of various methods of eco-efficiency estimation are shown in
307
Table 1.To summarize the previous literature, four aspects are highlighted. First, the literature on 11
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farming eco-efficiency is rich; however, few studies have focused on the eco-efficiency of grain
309
production at the macro-level. Second, the ratio method and DEA are two popular approaches for
310
these calculations. The ratio method is often used in the beginning of eco-efficiency assessments,
311
whereas the frontier-based approach has been more popular recently. Third, the single indicator
312
separately can only provide limited information. The aggregation of indicators requires appro-
313
priate methods. Fourth, SFA is another common efficiency estimation method in productivity
314
analyses, and it has some other advantages over DEA. On the one hand, SFA consider statistical
315
noise, unlike DEA, which attributes all deviations to inefficiency. On the other hand, as a paramet-
316
ric frontier approach, the SFA model involves the influencing factors analysis of eco-efficiency,
317
which makes it easy to ascertain policy variables that can be used to address eco-inefficiency.
318
However, few studies have used it to measure eco-efficiency so far. The reasons are as follows: 1)
319
SFA is a parametric frontier approach based on production function. Environmental performance
320
cannot be seen as the input of economic performance in general. 2) The first problem could be
321
handled by considering environmental impact as an undesirable output and economic performance
322
as a desirable output, as has been done in many improved DEA models. However, it cannot be im-
323
plemented because SFA can only analyse one-output models. Including green, blue and grey WFs
324
as inputs in the production function can make estimating eco-efficiency using SFA reasonable and
325
suitable, at least for grain production.
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According to these literatures review, scarce studies have analysed and evaluated eco-efficiency
327
using SFA, particularly for grain production at the regional level. In light of this gap in the liter-
328
ature and the relevance of this topic, a WF-SFA framework is proposed and applied to analyse
329
the regional eco-efficiency of grain production in China in this study. This research can provide
330
a basic framework on eco-efficiency evaluation of the grain production with the SFA technique,
331
which will feed into policy and strategic development.
332
3. Methodology and data
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This research applies two methods. First, the rainwater consumption (green WF), irrigation
334
water consumption (blue WF) and pollutant-assimilating water consumption (grey WF) related to
335
grain production are calculated based on a WF analysis. Second, a grain production SFA model 12
Table 1 Strengths and weaknesses of various methods of eco-efficiency estimation. The ratio method
The frontier-based approach
Method Aggregated indi-
Data
tor
cator
analysis (DEA)
Economic per f ormance Environmental in f luence
definition Features
envelopment
Stochastic
frontier
approach
SC
Single indica-
(SFA)
Actual output Frontier output
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It is often com-
It is nonparametric
It is parametric and stochastic,
bined with life cy-
and
using the econometric approach.
cle analyses.
using the mathematical
deterministic,
programming
It is straightfor-
and intuitive.
It is difficult
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Weaknesses
It is simple
1) It does not require
1) It offers a richer specification;
ward and compre-
any distributional as-
2) it allows for a formal statisti-
hensive.
sumptions about effi-
cal testing of hypotheses and the
ciency, and 2) it can
construction of confidence inter-
address the joint pro-
vals; and 3) the random effects
duction of multiple
can be separated from the contri-
outputs.
bution of variation in efficiency.
It does not consider
It is inflexible in addressing
statistical noises.
multiple outputs and the some-
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Strengths
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method.
Aggregating
the
to specify a
performances into
representative
one
meaningful
what arbitrary distribution as-
performance
indicator is often
sumptions regarding the ineffi-
indicator.
not possible.
ciency term.
13
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that considers three WFs (accounting for resources consumption and environmental impact) as
337
inputs is presented to assess eco-efficiency, identify the factors influencing eco-efficiency and cal-
338
culate the output elasticities of three WFs.
339
3.1. Method of assessing the grain production water footprint
340
341
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The total WF of grain production is the sum of the green, blue and grey water components (Hoekstra et al., 2011):
SC
T WF = GWF + BWF + EWF.
(1)
Where T WF is the total water footprint of grain production, m3 ; GWF is the green water footprint,
343
m3 ; BWF is the blue water footprint, m3 ; and EWF is the grey water footprint, m3 .
344
345
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Following Hoekstra et al. (2011), the green WF related to grain production is calculated as follows:
Pree · S . λ
(2)
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GWF = 346
Where Pree is the effective precipitation during the growth period of grain, mm; S is the sown area
347
of grain, ha; and λ is the multiple crop index of grain. The effective precipitation during the growth period of grain is calculated according to the
349
method developed by the USDA; effective rainfall can be calculated according to Doll and Siebert
350
(2002):
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351
352
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Pre · (125 − 0.2Pre) 125 Pree = 125 + 0.1Pre
Pre ≤ 250mm (3) Pre > 250mm
Where Pre is monthly precipitation, mm. The blue WF related to grain production is calculated as follows (Sun et al., 2016): BWF = I · S .
353
Where I is irrigation water use per unit of sown area of grain, m3 /ha. 14
(4)
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355
According to Hoekstra et al. (2011), the grey WF related to grain production is calculated as follows: EWF =
X i
! Poli · αi . Cimax − Cinat
(5)
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Where Pol is the consumption of pollutants, which consist of fertilizers, pesticides and insecti-
357
cides, kg; α is the leaching-run-off fraction, %; Cmax is the maximum acceptable concentration for
358
a pollutant, kg/m3 ; and Cnat is the natural concentration of a pollutant, kg/m3 .
359
3.2. Stochastic frontier production function model and eco-efficiency
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SFA is a parametric frontier approach developed independently by Aigner et al. (1977) and
361
Meeusen and van Den Broeck (1977). SFA starts with a standard cost or profit function and esti-
362
mates the minimum cost or maximum profit frontier for the entire sample from balance sheet data.
363
The efficiency measure for a specific observation is its distance from the frontier. Kumbhakar et al.
364
(1991) and Reifschneider and Stevenson (1991) proposed single-stage stochastic frontier function
365
models in which technical inefficiency effects were involved. Battese and Coelli (1992) and Bat-
366
tese and Coelli (1995) extended the previous two models for panel data. The decisive virtues of
367
SFA are that it covers both random noise, e.g., due to well-known measurement problems, and
368
systematic differences, e.g., due to heterogeneity across samples (Kumbhakar and Lovell, 2000).
369
These features allow for a relative comparison of markedly different samples. SFA is now a popu-
370
lar tool for benchmarking efficiency and productivity in many fields.
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360
Following Battese and Coelli (1995), a SFA model for grain production is established. The
372
schematic diagram of this model is as shown in Fig. 1. In the tth year, for the ith region, the basic
373
stochastic frontier production function is as follows:
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371
yit =exp(Xit β + vit − uit ), uit =Z it δ + wit .
(6)
374
Where yit denotes the production obtainable from Xit , a vector of values of inputs, and β is an
375
unknown parameter vector to be estimated. vit s are assumed to be iid N(0, σ2v ) random errors, 15
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Eco-efficiency:
Inputs,QGLFDWRUV:
the ratio of the actual output to the potential output.
Capital (K)
Output,QGLFDWRU:
Labour (L) Green water footprint (GWF)
Influence factors: Per capita GDP (PG) Grey water footprint (EWF)
Per capita water supply (PWS) Proportion of the irrigation area in the total cultivated area (IS)
Proportion of government expenditure on environmental protection relative to total government expenditure (ES)
M AN U
annual average temperature (T)
Proportion of the disaster area in the total sown area (DS)
SC
Blue water footprint (BWF)
RI PT
Output value of grain (Y)
Fig. 1. Framework of the stochastic frontier function model for grain production.
independently distributed of uit . uit s are non-negative random variables associated with the tech-
377
nical inefficiency of production and are assumed to be independently distributed, such that uit is
378
obtained by truncation of the normal distribution with zero mean, Z it δ, and variance, σ2u . Z it is a
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376
vector of explanatory variables associated with technical inefficiency, and δ is a vector of unknown
380
coefficients. The random variable, wit , is defined by truncation of the normal distribution with zero
381
mean and variance, σ2u , such that uit is a non-negative truncation of the N(Z it δ, σ2u ) distribution as
382
assumed. The disturbance uit reflects the fact that each output must lie on or below its frontier
383
Xit β + vit . The frontier is stochastic, with random disturbance vit being the result of favourable
384
as well as unfavourable external events such as climate, topography, and machine performance
385
(Aigner et al., 1977).
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386
Therefore, the eco-efficiency (the technical efficiency of ecological production) for the ith re-
387
gion at the tth observation point can be defined as the ratio of the actual output to the frontier
388
output: Eit =
389
exp(Xit β + vit − uit ) = exp(−uit ). exp(Xit β + vit )
(7)
This study specifies the stochastic frontier production function using the flexible translog spec16
ACCEPTED MANUSCRIPT
390
ification. The linearized version of the tanslog time-varying stochastic frontier production function
391
to be estimated is as follows:
j=1
5
5
5
1 1 XX 1X β jk ln x jit ln xkit + βtt t2 + β j ln x jit + βt t + β jt ln x jit t + vit − uit . (8) 2 j=1 k≥ j 2 2 j=1
RI PT
ln yit = β0 +
5 X
Where ln denotes the natural logarithm; x1 is the capital (K); x2 is labour (L); x3 is the green water
393
footprint (GWF); x4 is the blue water footprint (BWF); x5 is the grey water footprint (EWF); and
394
t is the time trend variable. The inefficiency effects are assumed to be defined by
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395
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392
uit = δ0 + δ1 PGit + δ2 PWS it + δ3 IS it + δ4 DS it + δ5 ES it + δ6 T it + δt t + wit .
(9)
Where PG is per capita GDP; PWS is the per capita water supply; IS is the proportion of the
397
irrigation area in the total cultivated area; DS is the proportion of the disaster area in the total
398
sown area; ES is the proportion of government expenditure on environmental protection relative
399
to total government expenditure; and T is the annual average temperature.
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396
The descriptions of the variables used in the grain production SFA model are shown as Table
401
2. The maximum likelihood method is used for simultaneous estimation of the parameters of
402
the stochastic frontier and the model for the inefficiency effects, defined by Eqs. 8 and 9. The
403
likelihood function is expressed in terms of the variance parameters, σ2 = σ2v + σ2u and γ = σ2u /σ2 .
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400
σ2 is the total variances of the error terms. γ is the ratio of variance in the inefficiency effects to
405
the total variances of the error terms.
406
3.3. Study area and data description
407
408
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404
The data used in this paper include a balanced panel consisting of annual time series for 31 provinces, autonomous regions and municipal cities in mainland China from 1997 to 2015.
409
In this paper, grain includes the three main crops: rice, wheat and maize. Only the consumption
410
of nitrogen fertilizer was considered for calculating the grey WF. Among the data used to estimate
411
the WF of grain production, the sown area of crops, the multiple crop index, the consumption
412
of nitrogen fertilizer and the total sown area were obtained from the Chinese National Bureau 17
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Table 2 Description and summary statistics for variables in the stochastic frontier production functions. Variable
Description
N
Mean
SD
Min
P50
Max
Output value of grain (billion yuan)
589
36.68
44.87
0.13
20.34
276.10
K
Capital (billion yuan)
589
14.04
16.49
0.06
7.96
93.25
L
Labour (million man − days)
589
376.00
309.10
3.14
338.40
1,900.00
GWF
Green water footprint (billion m3 )
589
9.62
7.98
0.11
8.79
37.01
BWF
Blue water footprint (billion m3 )
589
7.06
5.49
0.10
6.63
27.90
EWF
Grey water footprint (billion m3 )
589
9.79
8.63
0.07
7.10
39.22
PG
=
589
23,911.00 20,961.00 2,250.00 16,469.00 107,960.00
PWS
=
589
386.20
IS
=
DS
=
ES
=
T
Annual average temperature (◦C)
SC 423.50
M AN U
GDP Population (yuan/capita) Water supply 3 Population (m /capita) Irrigation area T otal cultivated area (%)
RI PT
Y
Disaster area T otal sown area (%) Government expenditure on environmental protection T otal government expenditure
(%)
40.00
276.10
2,657.00
589
55.10
23.03
17.37
57.71
100.00
589
14.39
10.35
0.00
11.92
62.31
589
1.42
1.67
0.00
0.00
6.73
589
14.36
5.04
4.30
15.10
25.40
of Statistics (1998-2016). The precipitation data were obtained from the China Meteorological
414
Data Service Center (1997-2015). The water consumption data were obtained from the Chinese
415
National Bureau of Statistics and Ministry of Environmental Protection (1998-2016). It is assumed
416
that on average 10% of the applied nitrogen fertilizer was lost through leaching or runoff, following
417
Zhang et al. (2015). The standard recommended by US-EPA is 10 mg per litre measured as nitrate-
418
nitrogen. The natural nitrogen concentrations were assumed to be zero.
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The total output value, total capital input and labour associated with grain were calculated
420
as the sum of the per ha values of the three main crops, rice, wheat and maize, weighted by
421
their sown area. The output value of the three main crops per ha, the capital input per ha for
422
the three crops (which includes the seed, manure, chemical fertilizer, film, pesticides, animal
423
power, mechanical power, irrigation and drainage, fuel, materials and other) and the labour per ha
424
for the three crops were obtained from the Price Division of Chinese National Development and
425
Reform Commission (1998-2016). The sown areas of the three main crops were obtained from
426
the Chinese National Bureau of Statistics (1998-2016). The output value and capital input of grain
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419
18
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427
were adjusted to values based on the 1997 price level by dividing by the agricultural production
428
price index and agricultural materials price index, which were obtained from the Chinese National
429
Bureau of Statistics (1998-2016). Among the possible factors that influence eco-efficiency, GDP and population (used to calcu-
431
late per capita GDP), the cultivated area (used to calculate the proportion of the irrigation area
432
in the total cultivated area), total sown area (used to calculate the proportion of the disaster area
433
in the total sown area), government expenditure on environmental conservation and total govern-
434
ment expenditure (used to calculate the proportion of government expenditure on environmental
435
protection relative to total government expenditure) and average annual temperature of the capital
436
city were obtained from the Chinese National Bureau of Statistics (1998-2016). The irrigated area
437
(used to calculate the proportion of the irrigation area in the total cultivated area) were obtained
438
from the Chinese Ministry of Agriculture (1998-2016). The water supply (used to calculate per
439
capita water supply) and the disaster area (used to calculate the proportion of the disaster area in
440
the total sown area) were obtained from the Chinese National Bureau of Statistics and Ministry
441
of Environmental Protection (1998-2016). Some data on the government expenditure on environ-
442
mental conservation from 1997 to 2006 were missing and filled by the difference interpolation
443
method. Table 2 shows summary statistics for the SFA model variables as well.
444
4. Results
445
4.1. Water footprint of grain production
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430
With the method presented in Section 3.1, the WFs of grain production in China were calcu-
447
lated. Fig. 2 shows the change in the grain production WF during the period 1997-2015. The grain
448
production WF exhibited a significant decrease before reaching a minimum value (714.60 billion
449
m3 ) in 2003. Since then, the grain production WF exhibited a slowly increasing trend. The reason
450
is that policies to encourage grain production, direct grain subsidy and exemption from agricul-
451
tural tax, are implemented since 2004. The grain production WF in 2015 was 894.72 billion m3 ,
452
which was 1.06 times that of the 1997 value. The trend is parallel with the change in the total
453
grain yield over the examined period. However, the total grain yield increased by a factor of 1.27
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19
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454
from 841.76 billion kg in 1997 to 894.72 billion kg in 2015. This implies that the water resource
455
use per grain production (unit WF of grain production) in China has decreased in recent years. Blue water footprint
Grain yield
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600
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Water footprint (billion m3)
800
400
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200
0
Grey water footprint
1000
750
500
Grain Yield (billion kg)
Green Water footprint
RI PT
1000
250
0
1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015
Fig. 2. Water footprint of grain production and grain yield in China.
The annual average grain production WF in China from 1997 to 2015 was 820.37 billion m3 :
457
the green WF was 36.36% (298.26 billion m3 ), the blue WF was 26.66% (218.72 billion m3 ), and
458
the grey WF was 36.98% (303.39 billion m3 ). The green, blue and grey WFs were steady over the
459
study period, with average rates of increase of 1.07%, 0.15% and 0.29%, respectively.
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460
The spatial distribution of the WF of crop production is shown in Fig. 3 and Fig. 4. The
461
provincial grain production WFs decreased from eastern to western China. This geographical
462
distribution feature of grain production WF is consistent with the regional climate, soil and natural
463
resources. Over the period 1997-2015, Henan was the province with the highest virtual water
464
consumption for grain production (71.43 billion m3 /year), followed by Jiangsu (64.90 billion
465
m3 /year) and Shandong (58.22 billion m3 /year). The three largest consumers are all located in 20
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466
the east. Tibet, Qinghai and Beijing consumed the smallest amount of virtual water for grain
467
production, with annual average WFs of 0.50, 1.17 and 2.30 billion m3 /year, respectively. 50
50 Heilongjiang
Heilongjiang
Jilin Xinjiang Inner Mongoria Beijing Tianjin Hebei Shanxi
GansuNingxia
Xinjiang
Liaoning
Inner Mongoria Beijing Tianjin Hebei Shanxi
GansuNingxia
Shandong
Qinghai
Liaoning
Shandong
Qinghai
Shaanxi
30
Henan
Tibet
Jiangsu Anhui Shanghai
Hubei
Sichuan Chongqing
Jiangxi Fujian
Zhejiang
Hunan
Guizhou
Yunnan
Jiangxi Fujian
Yunnan
Guangxi
20
Henan
Tibet
Zhejiang Hunan
Guizhou
Shaanxi
30
Jiangsu Anhui Shanghai
Hubei Sichuan Chongqing
Guangxi
20
Guangdong
Hainan
Guangdong
Hainan
Water footprint (2001-2005)
Water footprint (2006-2010)
60 10
Jilin
40
RI PT
40
50
10
40
60 40
20
20
100
Heilongjiang
120
80
100
120
SC
80
Jilin
40 Xinjiang
Liaoning Inner Mongoria
M AN U
Beijing Tianjin Hebei
Shanxi
Gansu Ningxia Qinghai
Shaanxi
30 Tibet
Henan
Shandong
Jiangsu Anhui Shanghai
Hubei
Sichuan
Chongqing
Zhejiang
Hunan
Guizhou
Jiangxi
Fujian
Yunnan
Guangxi
20
Hainan
Heilongjiang
Jilin
40 Xinjiang Inner Mongoria Beijing Tianjin Hebei Shanxi
GansuNingxia
Liaoning
Shaanxi
Henan
Tibet
Jiangsu Anhui Shanghai
Hubei Sichuan Chongqing
Zhejiang Hunan
Guizhou
Jiangxi Fujian
Yunnan Guangxi
20
Inner Mongoria Beijing Tianjin Hebei
20
100
80
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40
120
Shanxi
GansuNingxia
Liaoning
Shandong
Qinghai Shaanxi
30
Henan
Tibet
60
Jiangsu Anhui Shanghai
Hubei Sichuan Chongqing
Guizhou
Zhejiang Hunan
Jiangxi Fujian
Yunnan Guangxi
20
Guangdong
Hainan
Water footprint (2011-2015)
20
60 10
Jilin Xinjiang
40
Guangdong
Hainan
Water footprint (1997-2000)
80
Heilongjiang
40
Water footprint (1997-2015)
Shandong
Qinghai
10
50
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50
30
Guangdong
10
60 40 20
100
120
80
100
120
AC C
Fig. 3. Annual average water footprint of grain production.
468
Fig. 3 also shows the temporal evolution characteristics of provincial grain production WFs.
469
Comparing the WFs in the last 5 years (2011-2015) with those in the earliest 4 years (1997-
470
2000), the spatial distribution of the WFs in China has changed greatly. The three regions with
471
the largest decrease in grain production WFs were Beijing, Shanghai and Zhejiang. Their grain
472
production WFs decreased by 60.23%, 54.88% and 49.42%, respectively, from the earliest 4 years
473
to the last 5 years, and the biggest parts of these decreased occurred in 2001-2005. The three 21
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0
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25
2.3 2.7 3.3 0.5 1.2 4.6 6 10.3 10.6
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Ti b Q et in g Be hai i Sh jing an Ti gha an i H jin ai n N an in gx Sh ia a G nxi a C ns ho u ng Fu qin g G jian ui zh Z In he ou ne jia r M ng Xi ong nj o Sh iang ria a Li anx ao i n Ji ing an Yu gxi nn a Ji n G lin u G an ua gx ng i Si do ch ng u H an ub H ei H un ei an lo ng H jian eb g A ei Sh nhu an i Ji don an g g H su en an
The share of various water footprint (%) 75
50
23.3 23.6 28.4 28.4 28.6
M AN U
Total water footprint Green Water footprint
11.9
14.2 15.2
22
RI PT
Blue water footprint
39.2
34.5 35.5
58.2
36.7
17.5
Fig. 4. Provincial annual average water footprint and its distribution from 1997 to 2015. 60
44.7 44.8 47.4 48.6
41.5
40
20.5
20
0
Total water footprint (billion m3)
100
SC
ACCEPTED MANUSCRIPT
Grey water footprint
71.4
80
64.9
ACCEPTED MANUSCRIPT
regions are all rapidly developed industrial regions in China. Due to uneven development and
475
industrial distribution, in some industrially non-rapid developed provinces, such as Heilongjiang,
476
Inner Mongolia and Shanxi, crop production and related crop production WFs have grown rapidly
477
since 2006. The average annual WFs of grain production in Heilongjiang, Inner Mongolia and
478
Shanxi have increased from 25.95, 14.62 and 8.49 m3 /year in the period 2001-2005 to 71.13,
479
29.03 and 12.83 m3 /year in the period 2011-2015, respectively.
RI PT
474
The proportions of green, blue and grey WFs relative to the total, averaged over the period
481
1997-2015, were very different among provinces (Fig. 4 and 5). A high proportion of the green
482
WF relative to the total reflects the presence of intensive rain-fed farming. Guizhou had the high-
483
est green WF proportion (54.38%), followed by Heilongjiang (49.87%) and Anhui (48.44%). In
484
semi-arid northwest China, the green WF proportions were less than 20%; Xinjiang had the lowest
485
proportion (9.05%), followed by Ningxia (15.98%). However, a high proportion of blue WF rela-
486
tive to the total reflects the presence of intensive irrigated agriculture. In the semi-arid northwest,
487
the blue WF proportions exceeded 50%: Xinjiang had the highest proportion (64.53%), followed
488
by Ningxia (52.92%) and Tibet (51.97%). The three provinces with the lowest blue WF propor-
489
tions were Chongqing (11.36%), Henan (13.60%) and Shaanxi (15.57%). Some of the regions
490
with a low green or blue WF proportion had a high grey WF proportion, which implies that high
491
grey WF proportions exist in those regions, such as Beijing, Shanxi and Shaanxi, i.e., 52.50%,
492
51.01% and 50.58%, respectively. The three provinces with the lowest grey WF proportions were
493
Tibet (17.04%), Qinghai (18.89%) and Guangxi (20.84%).
494
4.2. Estimates of grain production stochastic frontier model
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480
495
Maximum likelihood estimates of the parameters of the grain production stochastic frontier
496
model were obtained using a modification of the computer program FRONTIER 2.0 (Coelli, 1996).
497
Because the model involves a large number of parameters, tests of several null hypotheses were
498
first considered to decide whether a simpler model would be an adequate representation of the data
499
(see Table 3). The hypotheses were tested using likelihood ratio tests. The likelihood ratio test
500
statistic is λ = −2[L(H0 ) − L(H1 )], where L(H0 ) and L(H1 ) are the values of the log-likelihood
501
function under the specifications of the null and alternative hypotheses, H0 and H1 , respectively. 23
ACCEPTED MANUSCRIPT
50 Heilongjiang
Jilin
40 Xinjiang
Liaoning Inner Mongoria Beijing Tianjin Hebei
Tibet
Shanxi
Shandong
Shaanxi Henan Jiangsu AnhuiShanghai Hubei Chongqing Sichuan Zhejiang Hunan Jiangxi Guizhou Fujian Yunnan GuangxiGuangdong
20
Hainan
Green water footprint proportion (%)
80
100
120
M AN U
50
SC
50 40 30 20 10
10
RI PT
Gansu Ningxia Qinghai
30
Heilongjiang
Jilin
40
Xinjiang
Liaoning Inner Mongoria Beijing Tianjin Hebei
Gansu Ningxia
Qinghai
30
Tibet
Shanxi
Shandong
Shaanxi Henan Jiangsu AnhuiShanghai Hubei Chongqing Sichuan Zhejiang
Hunan Jiangxi Guizhou Fujian Yunnan GuangxiGuangdong
20
TE D
Hainan
Blue water footprint proportion (%)
10
60 50 40 30 20
80
100
120
EP
50
Heilongjiang
Jilin
AC C
40
Liaoning Inner Mongoria Beijing Tianjin Hebei Gansu Ningxia Qinghai
30
Tibet
Shanxi
Shandong
Shaanxi Henan Jiangsu AnhuiShanghai Hubei Chongqing Sichuan Zhejiang Hunan Jiangxi Guizhou Fujian Yunnan GuangxiGuangdong
20
10
Xinjiang
Hainan
Grey water footprint proportion (%) 50 40 30 20 80
100
120
Fig. 5. The proportion of green, blue and grey water footprint relative to the total (%).
24
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502
If the null hypothesis is true, then λ has an approximate Chi-square distribution with degrees
503
of freedom equal to the number of restrictions. If the null hypothesis includes λ = 0, then the
504
asymptotic distribution is a mixed Chi-square distribution (Coelli, 1996).
RI PT
Table 3 Statistics for tests of hypotheses involving some coefficients of the stochastic frontier function. Hypothesis
Log-likelihood
Test statistics
(2)
(3)
(4)
320.401
H0 : γ = δ 0 = δ j = δ t = 0
229.257
H1 : β jk , 0 or βtt , 0 or β jt , 0
320.401
H0 : β jk = βtt = β jt = 0
Decision
182.288
20.972
Reject
113.676
20.972
Reject
31.104
20.972
Reject
12.894
20.972
Accept
170.384
8.273
Reject
263.563
H1 : βtt , 0 or β jt , 0
320.401
H0 : βtt = β jt = 0
304.849
H1 : β jt , 0
320.401
H0 : β jt = 0
313.954
H1 : β jt = 0 and δ0 , 0 or δ j , 0 or δt , 0 H0 : β jt = 0 and δ0 = δ j = δt = 0
313.954
228.762
TE D
(5)
H1 : γ , 0 or δ0 , 0 or δ j , 0 or δt , 0
M AN U
(1)
value
(1% level)
SC
function
Critical
The first hypothesis considered in Table 3 is that the inefficiency effects are not present in the
506
model (γ = δ0 = δ j = δt = 0). This null hypothesis was strongly rejected. The results suggest
507
that ecological inefficiency exists and that the SFA model is suitable. The second hypothesis, that
508
the second-order coefficients in the translog function are equal to zero (β jk = βtt = β jt = 0) and
509
hence that the Cobb-Douglas function applies, was rejected as well. Thus, the translog stochastic
510
frontier production function model provides an accurate specification for grain production data
511
in China. The third hypothesis, i.e., that the technical efficiency of ecological production does
512
not vary over time (βtt = β jt = 0), was rejected at the 1% significance level, while the fourth
513
514
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505
hypothesis, i.e., that the technical progress of ecological production is neutral (β jt = 0), was P accepted. This result suggests that deleting 21 5j=1 β jt ln x jit t from the original model defined by
515
Equation 8 makes the model simpler but still adequate as a representation of the data. With it,
516
as the alternative hypothesis, the fifth null hypothesis specifies that the inefficiency effects are not 25
ACCEPTED MANUSCRIPT
a linear function of selected influencing factors (δ0 = δ j = δt = 0). This null hypothesis was
518
rejected at the 1% level of significance, which indicates that the joint effects of these 7 explanatory
519
variables on the inefficiencies of ecological production are significant, although the individual
520
effects of one or more of the variables may not be significant. These results above imply that the
521
522
RI PT
517
model for describing the grain production in China cannot be adequately specified in terms of a P simpler model associated with these null hypotheses, except that 21 5j=1 β jt ln x jit t could be deleted, given the specifications of the translog stochastic frontier grain production model defined by Eqs.
524
8 and 9. The estimated results for this final selected model are presented in Table 4.
SC
523
As shown in Table 4, the γ value was 0.608, and it was significant at the 1% level. This finding
526
indicates that 60.8 percent of the variation in grain output can be attributed to the technical ineffi-
527
ciency of ecological production. It also confirmed the presence of the one-sided error component
528
in the model, thus rendering the use of the OLS estimation technique was inadequate in represent-
529
ing the data. In addition, σ2 was 0.028 and significant, indicating the correctness of the specified
530
assumptions of the distribution of the composite error term. These results imply that the model is
531
appropriate and creditable.
532
4.3. Eco-efficiency levels of grain production
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525
Grain production eco-efficiencies in China over the study period were estimated with Eq. 7
534
based on the SFA model results shown in Table 4. The results indicate that the eco-efficiencies
535
were within the range of 0.424-0.986 (Fig. 6), with an average value of 0.807. The closer the
536
value of eco-efficiency is to 1, the more efficient the region is, which means that the best utilize of
537
resources is to produce the maximum amount possible while minimizing the environmental impact
538
through pollutant emissions (Robaina-Alves et al., 2015). The results indicate that there is room
539
for China to improve its grain production eco-efficiency.
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533
540
The estimates of eco-efficiency varied considerably across both time periods and regions (Fig.
541
7 and 8). The average grain production eco-efficiency for the whole country decreased in the early
542
sample periods but subsequently improved, and this trend of fluctuating improvement was also
543
observed in east, central and west China. East China has a higher grain production eco-efficiency
544
than central and west China. 26
ACCEPTED MANUSCRIPT
Table 4
Variable
Parameter
Estimate
t-ratio
ln GWF · ln EWF
Stochastic Frontier Production Function: Constant
β0
0.781
ln K
β1
ln L
∗
Variable
RI PT
Results of the stochastic frontier production function model. Parameter
Estimate
t-ratio
β35
-0.334∗∗∗
-2.757
β44
-0.009
-0.221
β45
0.193∗∗
2.131
β55
-0.172∗
-1.934
βtt
0.004∗∗∗
4.619
1.765
ln BWF · ln BWF
1.160∗∗∗
9.373
ln BWF · ln EWF
β2
0.063
0.293
ln EWF · ln EWF
ln GWF
β3
0.077
0.338
t2
ln BWF
β4
0.181
1.394
Inefficiency Model:
ln EWF
β5
-0.605∗∗∗
-4.047
Constant
δ0
0.19726∗
t
βt
-0.023∗∗
-2.209
PG
δ1
-0.00001∗∗∗
-6.561
ln K · ln K
β11
0.041
PWS
δ2
-0.00046
∗∗∗
-8.621
ln K · ln L
β12
-0.220∗∗∗
IS
δ3
0.00003
ln K · ln GWF
M AN U
1.019
-3.807
β13
0.156
∗∗
2.399
DS
ln K · ln BWF
β14
-0.092∗
-1.821
ln K · ln EWF
β15
ln L · ln L
β22
ln L · ln GWF
β23
ln L · ln BWF
β24
ln L · ln EWF
β25
ln GWF · ln GWF
β33
β34
0.00789
6.353
ES
δ5
-0.03647∗∗
-2.428
T
δ6
∗∗∗
0.00859
2.616
δt
0.01637∗
1.716
0.028∗∗∗
10.432
∗∗∗
11.012
TE D
δ4
0.018
0.320
t
0.022
0.197
Variance Parameter:
-1.952
σ2
4.585
γ
0.608
0.056
0.711
Log-likelihood fuction
313.954
0.007
0.109
LR test
189.39
-0.118∗
0.362
∗∗∗
0.041
∗∗∗
AC C
Note:
1.285
EP
ln GWF · ln BWF
0.086
SC
1.889
1. Y: output value of grain; K: capital; L: labour; GWF: green water footprint; BWF: blue water footprint; EWF: grey water footprint; t: time; PG: per capita GDP; PWS : per capita water supply; IS : proportion of the irrigation area in the total cultivated area; DS : proportion of the disaster area in the total sown area; ES : proportion of government expenditure on environmental protection relative to total government expenditure; T : annual average temperature. 2. σ2 : total variances of the error terms. 3. γ: ratio of variance in the inefficiency effects to the total variances of the error terms. 4.
∗∗∗
: significant at 1%, ∗∗ : significant at 5%, ∗ : significant at 10%.
27
ACCEPTED MANUSCRIPT
5%
RI PT
4%
Density
3%
2%
SC
1%
0%
M AN U
0.40 0.45 0.50 0.55 0.60 0.65 0.70 0.75 0.80 0.85 0.90 0.95 1.00
Fig. 6. Distribution of grain production eco-efficiency in China.
0.9
TE D
0.8
0.716
EP
0.751
0.7
0.65
0.6
0.933 0.916 0.906
0.876
0.873
0.841
0.808
0.795
AC C
Eco-efficiency
0.881
0.936 0.935 0.926
0.725
East China
0.647 0.622
Central China
West China
China
0.595
1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015
Fig. 7. Grain production eco-efficiency from 1997 to 2015.
28
50
50 Heilongjiang
Heilongjiang
Jilin
40 Xinjiang
Jilin
40 Xinjiang
Shanxi
Gansu Ningxia Qinghai
Shaanxi
Hubei SichuanChongqing
30
Jiangsu Anhui Shanghai
Shaanxi
Henan
Tibet
Hubei SichuanChongqing
Zhejiang
Hunan Jiangxi Fujian
Guizhou
Shandong Jiangsu Anhui Shanghai Zhejiang
Hunan Jiangxi Fujian
Yunnan
Guangxi
20
Guangdong
Hainan
Eco-efficiency (2001-2005)
Guangxi
M AN U
Yunnan
20
Shanxi
Gansu Ningxia
Shandong
Qinghai
Henan
Tibet
Guizhou
Liaoning Inner Mongoria Beijing Tianjin Hebei
SC
Liaoning Inner Mongoria Beijing Tianjin Hebei
30
RI PT
ACCEPTED MANUSCRIPT
Eco-efficiency (2006-2010)
0.9
Guangdong
Hainan
0.95
10
0.8
10
0.90 0.85
0.7
0.80
0.6 80
100
120
50
80
100
120
50
Heilongjiang
TE D
Heilongjiang
Jilin
40 Xinjiang
Shanxi
Gansu Ningxia Qinghai
Shaanxi
30 Tibet
Henan
Hubei SichuanChongqing Guizhou Yunnan
Jiangsu Anhui Shanghai
AC C
0.7
100
Henan
Tibet Hubei SichuanChongqing Guizhou
Shandong Jiangsu Anhui Shanghai Zhejiang
Hunan Jiangxi Fujian
Yunnan Guangxi
Guangdong
Hainan
Eco-efficiency (2011-2015) 0.95 10
0.90 0.85
0.6
80
Shaanxi
20
Guangdong
Shanxi
Gansu Ningxia Qinghai
30
Zhejiang
EP
10
Liaoning Inner Mongoria Beijing Tianjin Hebei
Shandong
Hainan
Eco-efficiency (1997-2000) 0.8
Xinjiang
Hunan Jiangxi Fujian
Guangxi
20
Jilin
40
Liaoning Inner Mongoria Beijing Tianjin Hebei
0.80 120
80
100
Fig. 8. Annual average grain production eco-efficiency in provinces of China.
29
120
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The spatiotemporal evolution of grain production eco-efficiency is shown in Fig. 8. The em-
546
pirical evidence shows that the provincial grain production eco-efficiencies have improved signif-
547
icantly over the study period, especially in the last 5 years (2011-2015). In the earliest 4 years
548
(1997-2000), Shanghai (0.873), Xinjiang (0.768) and Zhejiang (0.762) were the three most effi-
549
cient provinces while Shaanxi (0.561), Hubei (0.576) and Shanxi (0.588) constituted the least effi-
550
cient provinces. This current pattern has changed greatly. The eco-efficiencies of Xinjiang (0.985)
551
and Shanghai (0.980) have steadily improved, and they were still two most efficient provinces
552
in the last 5 years (2011-2015). Some other provinces have made great progress and achieved
553
very high eco-efficiencies, such as Jiangsu (0.977), Inner Mongolia (0.977) and Beijing (0.976).
554
In addition to Inner Mongolia (increased by 0.327), Hubei (increased by 0.361), Jilin (increased
555
by 0.327) and Tibet (increased by 0.324) were the other provinces with the largest increases in
556
grain production eco-efficiency. In contrast, small improvement in the eco-efficiencies in Hainan,
557
Guizhou and Yunnan (increased by 0.112, 0.110 and 0.160, respectively) made them become the
558
three least efficient provinces in the period 2011-2015.
559
4.4. Determinants of grain production eco-efficiency
TE D
M AN U
SC
RI PT
545
The eco-efficiency indicator provides the overall outcome for the economic and environmental
561
efficiency of the joint use of production factors. It is also important to know what factors underline
562
the good or poor performance of the provinces mentioned. Given the model for the inefficiency
563
effects, defined by Eq. 9, the δ estimates are of particular interest. The maximum likelihood
564
estimates of the determinants of the eco-efficiency of grain production are presented in Table 3.
565
The results indicate that the grain production eco-efficiency tended to be higher for regions with
566
higher per capita GDP, greater per capita water supply, and a higher proportion of government
567
expenditure on environmental protection. The grain production eco-efficiency will increase with
568
a decrease in the proportion of disaster areas and temperature. The coefficients of the proportion
569
of the irrigation area relative to the total cultivated area had positive signs but were not significant.
570
The positive coefficient for t suggests that the ecological inefficiencies of grain production tended
571
to increase throughout the study period when other variables were controlled.
AC C
EP
560
30
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572
5. Discussions
573
5.1. Comparison of the results Although there is no known research calculating the eco-efficiency of grain production using
575
the SFA technique, many researchers have analysed eco-efficiency related to farming at different
576
levels. As shown in Table 5, the subjects of these studies are different. Thus, the results cannot be
577
compared directly, especially with eco-efficiency defined as a relative comparison indicator, as in
578
this study.
RI PT
574
This paper introduced a way of measuring eco-efficiency based on the SFA technique. The ratio
580
method and DEA are the two main methodologies used in previous studies. The ratio methodology
581
proposes a single efficiency measure to select one solution out of a set of solutions according to
582
the highest (economic value / environmental pressure) ratio (Quariguasi Frota Neto et al., 2009).
583
However, it cannot differentiate between different environmental impacts, and it requires subjec-
584
tively assigned weights by the estimator. In mathematical terms, the ratio procedure is a DEA
585
model with one input (environmental performance), one output (economic performance) and con-
586
stant returns of scale (Quariguasi Frota Neto et al., 2009). DEA can provide a more comprehensive
587
result for eco-efficiency evaluation when more than one input or output is considered, as Hoang
588
and Alauddin (2012) has done. This approach does not require any distributional assumptions
589
about efficiency; because no stochastic specification is imposed, all variation between production
590
units is interpreted as inefficiency (Hjalmarsson et al., 1996). SFA can overcome this shortcom-
591
ing. However, defining an appropriate and reasonable production function, as is necessary in SFA
592
analysis, is difficult. The analytical thinking in Carberry et al. (2013) is very similar to SFA, con-
593
sidering only one input (nitrogen) and one output (grain yield). However, the production frontiers
594
are simulated with the Agricultural Production Systems Simulator, not with data, as in SFA. Thus,
595
it is impossible to determine which approach is better than the other because the true level of ef-
596
ficiency is unknown. In general, all deviations from the frontier are interpreted as inefficiency in
597
DEA, so the SFA approach normally yields lower inefficiency levels (Battese et al., 2000).
AC C
EP
TE D
M AN U
SC
579
598
A translog stochastic frontier function was adopted to estimate the relative eco-efficiency of
599
provincial grain production in this study. The output frontier is the potential grain output, while 31
ACCEPTED MANUSCRIPT
Table 5
Literature Reith
and
Scale
Object
Method
Farm
An agricultural research com-
Ratio method
Guidry (2003)
plex:
RI PT
Summary of previous farming eco-efficiency research. Definition of eco-efficiency
the Model Sustainable
A ratio of product delivered (calories of food energy) relative to re-
Agricultural Complex
sources consumed (calories of elec-
Farm
Farms belonging to the rain-fed
Data envelop-
A ratio between economic value
agricultural system of Campos
ment analysis
added and environmental pressures
County in Palencia, Spanish
(DEA)
National systems of crop and
Input-
The ratio of the smallest total cu-
Alauddin
livestock production in 30 OECD
orientated
mulative exergy amount to the ob-
(2012)
countries from 1990 to 2003
DEA
served cumulative exergy amount
Olive farms in Andalusia
DEA
A ratio between net income and a
et al. (2011)
Hoang
and
G´omez-Lim´on
Country
Farm
M AN U
Picazo-Tadeo
SC
trical or fossil energy)
Carberry et al.
Farm
(2013)
et al. (2014) M¨uller et al.
Comparison of crop yields against
China, Zimbabwe and Australia
production
simulated grain yields at farmer-
frontiers
specified levels of nitrogen input
Ratio method
Total value product per environ-
Paddy rice production in northeastern Thailand
Farm
mental impact
Kiwifruit production in New
Ratio method
Zealand
Beltr´an-Esteve
Farm
Region
Net profit per kg greenhouse gas emissions
Spanish citrus farms
DEA
A ratio between economic value
et al. (2017)
This study
mance
Compare with
AC C
(2015)
Country
measure of environmental perfor-
Diverse cropping systems in
EP
Thanawong
TE D
et al. (2012)
and an aggregate of damaging environmental impacts arising from farms’ economic activity
Grain production in China
The ratio of the actual output to the
Stochastic frontier
ap-
proach (SFA)
32
potential output
ACCEPTED MANUSCRIPT
the input is expressed in terms of five variables: capital, labour, green WF, blue WF and grey WF.
601
The selection of indicators is very different from previous studies mainly in environmental per-
602
formance indicators, although the concept of involving resource consumption and environmental
603
impacts in farming production is common. Some previous studies have focused on the individual
604
environmental performance of farming, such as the electrical or fossil energy consumed (Reith and
605
Guidry, 2003), cumulative exergy amount (Hoang and Alauddin, 2012), nitrogen (Carberry et al.,
606
2013), and greenhouse gas emissions (M¨uller et al., 2015). Others have used a comprehensive
607
environmental indicator (Picazo-Tadeo et al., 2011; Thanawong et al., 2014). The eco-efficiency
608
assessment comprises comprehensive efficiency of economics, resources and the environment in
609
this paper. It is achieved by combining traditional production factor inputs (capital and labour)
610
with natural water resources (green water), irrigation water resources (blue water) and the envi-
611
ronmental impact of the overuse of fertilizers, pesticides, etc. (grey water).
M AN U
SC
RI PT
600
Furthermore, the path for moving closer to the efficiency frontier is analysed in this paper by an
613
influencing factors analysis. Farmer feature and farm feature variables have been used to analyse
614
the determinants of eco-efficiency using truncated regression and bootstrapping techniques, and
615
the results show that farmers who benefit from agri-environmental programs as well as those with
616
a university-level education were found to be more eco-efficient (Picazo-Tadeo et al., 2011). The
617
result in G´omez-Lim´on et al. (2012) is that soil-climate conditions strongly influence managerial
618
eco-efficiency. Unlike previous studies, this study investigated the determinants of eco-efficiencies
619
at a regional level; hence, some macro variables were selected as possible factors. The results
620
show that the per capita GDP, per capita water supply, proportion of government expenditure
621
on environmental protection and proportion of non-disaster areas positively influenced the grain
622
production eco-efficiency.
623
5.2. Output elasticity of inputs
AC C
EP
TE D
612
624
The output elasticity analysis of individual inputs can address key factors for improving eco-
625
efficiency. It is implementable with the SFA technique. Based on Eq. 8, the elasticity of output
33
ACCEPTED MANUSCRIPT
626
with respect to the jth input ( j ) is calculated as j =
1X ∂ ln y = βj + β jk ln xk + β j j ln x j . ∂ ln x j 2 j,k
(10)
With Eq. 10 and estimate results of grain production SFA model shown in Table 3, the output
628
elasticities of the five input variables over time in China are calculated. The results are presented
629
in Fig. 9. The returns to scale (sum of all output elasticities of inputs) were estimated to be greater
630
than one for all years, which indicates that there were increasing returns to scale; increasing the
631
inputs by 1% would produce a more than 1% increase in output.
0.75
SC
Grey water footprint
AC C
0.00
Blue water footprint
TE D
0.50
0.25
Green water footprint
M AN U
Labor inout
EP
The elasticity of output with respect to the inputs
Capital stock
RI PT
627
1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015
Fig. 9. The elasticities of output with respect to the inputs.
632
Regarding individual input, the output elasticities of capital were the highest, and they in-
633
creased continuously over the study period. However, the output elasticities of labour tended
634
to decrease over time. These results imply that mechanization was the driving force behind the
635
grain production growth in China. The output elasticity of the green WF was greater than zero 34
ACCEPTED MANUSCRIPT
and increased steadily. However, the output elasticity of the blue WF was estimated as negative
637
throughout the study period. These results reflect the fact that China’s grain production pattern
638
runs counter to the distribution patterns of water-heat conditions (Li et al., 2017). The grain pro-
639
duction barycenter in China has been moving northwards continuously over time (Wang et al.,
640
2018). The conditions of water and temperature in the northern region are worse than those in
641
the southern region. This situation explains why increasing blue WF cannot increase overall grain
642
output in China. Another reason is the differences in crop structure among regions. Maize, which
643
has a lower output value than other grains, plays a dominant role in North China’s grain output
644
(Wang et al., 2018). The output elasticity of the grey WF decreased over the study period and
645
has decreased below zero in recent years. These observations imply that increasing the amount
646
of chemical fertilizer cannot increase grain output; rather, it will decrease grain output. Chemical
647
fertilizers continue to be overused in China, and this overuse has resulted in a series of environ-
648
mental consequences (Jiao et al., 2018). The main reason for this overuse is a lack of awareness
649
of nutrient management and environmental protection. The existence of negative output elastici-
650
ties of the blue and grey WFs implies that improving irrigation water productivity and controlling
651
environmental cost are urgent for grain output growth. The output elasticities of the three WFs
652
according to the different provinces are calculated for addressing the key regions (Fig. 10).
TE D
M AN U
SC
RI PT
636
As shown in Fig. 10, the annual average output elasticity of the green WF was positive in
654
all provinces. The three provinces with the largest output elasticities of the green WF were Tibet
655
(0.270), Qinghai (0.258) and Heilongjiang (0.197). The annual average output elasticities of the
656
blue WF were negative in some provinces. Controlling water logging and adjusting crop con-
657
struction are necessary for those provinces, which include Gansu (-0.142), Guangxi (-0.128) and
658
Jiangxi (-0.126). In provinces with negative output elasticities of the grey WF, such as Henan
659
(-0.163), Jiangsu (-0.137) and Anhui (-0.131), more fertilizer input will produce less grain. These
660
provinces are the targets of environmental conservation with respect to grain production.
661
5.3. Changes in eco-efficiency and total factor productivity
AC C
EP
653
662
There was some confusion regarding the technical efficiency and total factor productivity. In
663
this section, the change in eco-efficiency and the relationship between eco-efficiency and total 35
0.2
0.1
0.0
-0.1
EP TE D
0.3
AC C
-0.15 G an G su ua n Ji gxi an G gxi u H izh ei o lo u n Q gjia in ng g Y h In un ai ne n r_ an M Si ong ch o u ri H an a un Sh an an Ti xi C be ho t ng N qin in g gx An ia hu Ji i l Xi in n Sh jian a g G nd ua on ng g d H on eb g H ei en H an ai n Fu an jia H n ub Sh ei a Li anx ao i Zh nin ej g i Ji ang an Sh gs an u g Ti ha an i Be jin ijin g
Elasticity of blue water footprint 0.00
-0.05
-0.10
M AN U
0.05
SC
Elasticity of green water footprint 0.1
RI PT
an g H su en Sh an aa H nxi eb Be ei ijin H g ub Sh e an i d Ti ong an Xi jin nj ia Fu ng G jia ua n ng Si do ch ng Li uan ao Zh nin ej g ia Sh ng a C nx ho i ng q Ji ing lin N in gx An ia h H ui un Sh an an Yu gha In ne nn i r_ an M o H ng ai o na ria G n an G su ua G ngx ui i zh Ji ou a H ng ei x lo i ng Q jia in ng gh Ti ai be t
Ji
0.0
H en Ji an an g An su hu Be i ijin H g ub Li ei ao Sh nin an g do Ji ng lin Ti an Sh jin aa H nxi e Zh bei e G jia ua ng H ngd ei lo on n g Sh gjia an ng C gh ho a ng i H qin un g a Fu n jia Si n ch Yu uan nn H an ai n G a In uiz n ne h r_ ou M o Sh ng an ori Ji xi a an G gxi ua n Xi gx nj i ia N ng in g G xia an s Ti u be Q t in gh ai
Elasticity of grey water footprint
ACCEPTED MANUSCRIPT
0.2
Fig. 10. The provincial output elasticities of water footprints.
36
ACCEPTED MANUSCRIPT
664
factor productivity are discussed. According to Kumbhakar and Lovell (2000) and Kim and Han
665
(2001), the logarithm of y in Eq. 8 is completely differentiated with respect to time to obtain
∂ ln y ∂t
(11)
RI PT
d ln y ∂ ln y X d ln x j du = + − . j dt ∂t dt dt j
j
d ln x j dt
666
Where d dtln y measures the output change rate;
667
measures the change rate of ecological inefficiency. measures the change rate of input use; and − du dt
668
The change rate for total factor productivity is defined as the output change rate, which is unexplained by the input change rate:
M AN U
d ln x j d ln y X d ln x j ∂ ln y du X − Sj = − + j − S j dt dt ∂t dt dt j j ! X j d ln x j X j d ln x j ∂ ln y du X P P −Sj − + j − 1 + = ∂t dt dt dt j j j j j j j
T FPC =
P j
SC
669
measures the rates of technical progress;
(12)
= T P + EC + S C + AEC.
672
673
674
TE D
671
Where T FPC is the change rate of total factor productivity; S j is input j’s proportion of proP duction costs; j represents the input elasticities defined at the production frontier; j j denotes the measurement of returns to scale; T P is the rates of technical progress; EC is the change P j d ln x j P rate of ecological inefficiency; S C = represents scale components; and j P j j dt j j − 1 P j d ln x AEC = j P j j − S j dt j represents the allocative efficiency change rate, which measures in-
EP
670
efficiency in resource allocation resulting from deviations of input prices from the value of their
676
marginal product. Thus, in Eq. 12, the change rate for total factor productivity can be decomposed
677
into rates of technical progress, the eco-efficiency change rate, scale components and the allocative
678
efficiency change rate.
679
AC C
675
The technology progress is calculated as 5
TP =
∂ ln y 1X = βt + β jt ln x j + βtt t. ∂t 2 j=1
37
(13)
ACCEPTED MANUSCRIPT
The eco-efficiency change rate is calculated as EC =
681
Et − Et−1 . Et
(14)
The change rate of inputs is calculated as d ln x j ln x jt − ln x j(t−1) = . dt ln x j(t−1)
RI PT
680
(15)
The components of the change rate for total factor productivity described in Eq. 12 are then
683
calculated for each region over time based on the results in Table 3. The allocative efficiency was
684
not calculated because the cost of virtual water use was not available. Fig. 11 presents the aver-
685
ages of the rates of technical progress, the eco-efficiency change rate, the scale components and
686
the total factor productivity growth. Technical progress and eco-efficiency were the most impor-
687
tant aspects of total factor productivity. The annual average total factor productivity growth rate
688
was estimated as 0.034 over the period 1997-2008, with 40.64% attributed to technical progress
689
(0.014), 60.20% to eco-efficiency (0.021), and only -0.84% to scale components (-0.0003). The
690
rates of technical progress became positive in 2003 and increased continuously during the study
691
period. Eco-efficiency change was the key driver of total factor productivity, and it consistently
692
had the same direction.
AC C
EP
TE D
M AN U
SC
682
38
ACCEPTED MANUSCRIPT
The total factor productivity
Technology progress
Eco-efficiency
Scale Composition
RI PT
0.2
SC
Change rate
0.1
M AN U
0.0
-0.1
1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015
693
TE D
Fig. 11. The decomposition of total factor productivity.
6. Conclusions and implications
The eco-efficiency is a major access to achieve sustainability. Eco-efficiency assessment can
695
provide policymakers with reliable information to design environmentally sustainable managerial
696
strategies and policies. The present study developed an integrated WF-SFA method by combining
697
WF estimation and the SFA model to estimate the eco-efficiency of grain production in China.
698
First, the green, blue and grey WFs, representing natural water resources, irrigation water re-
699
sources and environmental impact of grain production receptivity, respectively, were calculated
700
by a WF analysis. Then, by identifying the three WFs plus capital and labour as inputs and grain
701
output value as the only output, a stochastic frontier function of grain production was established
702
to estimate eco-efficiency. Eco-efficiency was defined as the ratio of the actual output to the po-
703
tential output, and it was estimated with the SFA technique. The eco-inefficiency effects were also
704
modelled using six influencing factors.
AC C
EP
694
39
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The main findings are summarized as follows. First, the average annual grain production WF
706
in China from 1997 to 2015 was 820.37 billion m3 . Specifically, 36.36% was green WF, 26.66%
707
was blue WF, and 36.98% was grey WF. Second, the eco-efficiencies were estimated to be within
708
the range of 0.424-0.986, with an average value of 0.807. There is potential for China to make its
709
grain production system more environmentally and ecologically sustainable. Third, the per capita
710
GDP, per capita water supply, proportion of government expenditure on environmental protection
711
and proportion of disaster areas are key factors affecting eco-efficiency at the regional level. In
712
addition, the output elasticity analysis showed that the blue and grey WFs have negative output
713
elasticities in recent years. They are key inputs that need to be controlled for eco-efficiency im-
714
provement. China must feed its large population with minimizing the environmental impact while
715
helping to sustainable development goals as well. These findings can help China design relevant
716
policies of agricultural sustainability focused on crop distribution, efficient irrigation water use
717
and nutrient and pollutant management.
M AN U
SC
RI PT
705
This study provides a new method for the eco-efficiency assessment of grain production that
719
could potentially be applied in other fields. It also faced some limitations. One weakness of SFA
720
is that if the functional form is specified incorrectly, then the measured efficiency may be con-
721
founded with the specification errors. Although the WF-SFA framework used in this study has
722
been proven suitable for eco-efficiency assessments of grain production, transferring it to other
723
fields may involve huge challenges. Moreover, the resource consumption and environmental im-
724
pact related to grain production are very complicated and can be difficult to quantify. This study
725
considers a small number resources and environmental indicators. Future studies should address
726
these limitations by improving SFA and by taking into account additional environmental impacts
727
of production systems.
728
Acknowledgments
AC C
EP
TE D
718
729
This work is supported by the National Natural Science Foundation of China (71503202,
730
71573208), the Humanities and Social Sciences Foundation of the Ministry of Education of China
731
(17YJC790126) and the Science Foundation of Shaanxi Province of China (2016JQ7001). 40
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732
References
733
Aigner, D., Lovell, C., Schmidt, P., 1977. Formulation and estimation of stochastic frontier production function
735 736
models. J. Econom. 6 (1), 21–37. Alves, J. L. S., Dumke De Medeiros, D., 2015. Eco-efficiency in micro-enterprises and small firms: A case study in the automotive services sector. J. Clean. Prod. 108, 595–602.
RI PT
734
737
Aoe, T., 2007. Eco-efficiency and ecodesign in electrical and electronic products. J. Clean. Prod. 15 (15), 1406–1414.
738
Basset-Mens, C., Ledgard, S., Boyes, M., 2009. Eco-efficiency of intensification scenarios for milk production in New
742 743 744 745 746 747 748 749 750 751
to paddy farmers in India. J. Prod. Anal. 3 (1-2), 153–169.
SC
741
Battese, G. E., Coelli, T. J., 1992. Frontier production functions, technical efficiency and panel data: With application
Battese, G. E., Coelli, T. J., 1995. A model for technical inefficiency effects in a stochastic frontier production function for panel data. Empir. Econ. 20 (2), 325–332.
M AN U
740
Zealand. Ecol. Econ. 68 (6), 1615–1625.
Battese, G. E., Heshmati, A., Hjalmarsson, L., 2000. Efficiency of labour use in the Swedish banking industry: A stochastic frontier approach. Empir. Econ. 25 (4), 623–640.
Beltr´an-Esteve, M., G´omez-Lim´on, J. A., Picazo-Tadeo, A. J., Reig-Mart´ınez, E., 2014. A metafrontier directional distance function approach to assessing eco-efficiency. J. Prod. Anal. 41 (1), 69–83. Beltr´an-Esteve, M., Reig-Mart´ınez, E., Estruch-Guitart, V., 2017. Assessing eco-efficiency: A metafrontier directional distance function approach using life cycle analysis. Environ. Impact Asses. 63, 116–127.
TE D
739
Bogetoft, P., Otto, L., 2011. Benchmarking with DEA, SFA and R. International Series in Operations Research & Management Science. Springer, New York.
Caiado, R. G. G., de Freitas Dias, R., Mattos, L. V., Quelhas, O. L. G., Leal Filho, W., 2017. Towards sustainable
753
development through the perspective of eco-efficiency - A systematic literature review. J. Clean. Prod. 165, 890–
754
904.
756
Camarero, M., Castillo, J., Picazo-Tadeo, A. J., Tamarit, C., 2013. Eco-Efficiency and Convergence in OECD Countries. Environ. Resour. Econ. 55 (1), 87–106.
AC C
755
EP
752
757
Carberry, P. S., Liang, W.-l., Twomlow, S., Holzworth, D. P., Dimes, J. P., McClelland, T., Huth, N. I., Chen, F.,
758
Hochman, Z., Keating, B. A., 2013. Scope for improved eco-efficiency varies among diverse cropping systems. P.
759
Natl. Acad. Sci. USA 110 (21), 8381–8386.
760
Cerutti, A. K., Beccaro, G. L., Bagliani, M., Donno, D., Bounous, G., 2013. Multifunctional Ecological Footprint
761
Analysis for assessing eco-efficiency: A case study of fruit production systems in Northern Italy. J. Clean. Prod.
762
40, 108–117.
763 764
Cha, K., Lim, S., Hur, T., 2008. Eco-efficiency approach for global warming in the context of Kyoto Mechanism. Ecol. Econ. 67 (2), 274–280.
41
ACCEPTED MANUSCRIPT
766 767 768 769 770
Charmondusit, K., Keartpakpraek, K., 2011. Eco-efficiency evaluation of the petroleum and petrochemical group in the map Ta Phut Industrial Estate, Thailand. J. Clean. Prod. 19 (2-3), 241–252. Charmondusit, K., Phatarachaisakul, S., Prasertpong, P., 2014. The quantitative eco-efficiency measurement for small and medium enterprise: A case study of wooden toy industry. Clean Technol. Envir. 16 (5), 935–945. China Meteorological Data Service Center, 1997-2015. Meteorological Data Set. Available at: http://data.cma.
RI PT
765
cn/data/cdcindex/cid/6d1b5efbdcbf9a58.html. (access on 6/22/2016).
771
Chinese Ministry of Agriculture, 1998-2016. China Agriculture Yearbook. China Agriculture Press, Beijing.
772
Chinese National Bureau of Statistics, 1998-2016. China Statistical Yearbook. China Statistics Press, Beijing.
773
Chinese National Bureau of Statistics, Ministry of Environmental Protection, 1998-2016. China Statistical Yearbook
776 777 778 779 780
SC
775
on the Environment. China Statistics Press, Beijing.
Coelli, T. J., 1995. Recent developments in frontier modelling and efficiency measurement. Aust. J. Agr. Ecno. 39 (3), 219–245.
M AN U
774
Coelli, T. J., 1996. A Guide to Frontier Version 4.1: A Computer Program for Stochastic Frontier Production and Cost Function Estimation. CEPA Working Papers WP7/96, University of Queensland. 1–13. Coelli, T. J., Rao, D. P., O’Donnell, C. J., Battese, G. E., 2005. An Introduction to Efficiency and Productivity Analysis, 2nd Edition. Springer, New York.
781
Doll, P., Siebert, S., 2002. Global modeling of irrigation water requirements. Water Resour. Res. 38 (4), 1–10.
782
Egilmez, G., Park, Y. S., 2014. Transportation related carbon, energy and water footprint analysis of U.S. manufac-
786 787 788 789 790 791
TE D
785
Farrell, M. J., 1957. The Measurement of Productive Efficiency. J. Royal Stat. Society. Series A (General) 120 (3), 253–290.
G´omez-Lim´on, J. A., Picazo-Tadeo, A. J., Reig-Mart´ınez, E., 2012. Eco-efficiency assessment of olive farms in Andalusia. Land Use Policy 29 (2), 395–406.
EP
784
turing: An eco-efficiency assessment. Transport Res. D-Tr. E. 32, 143–159.
Hjalmarsson, L., Kumbhakar, S. C., Heshmati, A., 1996. DEA, DFA and SFA: A comparison. J. Prod. Anal. 7 (2-3), 303–327.
Ho, T. Q., Hoang, V. N., Wilson, C., Nguyen, T. T., 2018. Eco-efficiency analysis of sustainability-certified coffee
AC C
783
production in Vietnam. J. Clean. Prod. 183, 251–260.
792
Hoang, V. N., Alauddin, M., 2012. Input-Orientated Data Envelopment Analysis Framework for Measuring and De-
793
composing Economic, Environmental and Ecological Efficiency: An Application to OECD Agriculture. Environ.
794
Resour. Econ. 51 (3), 431–452.
795
Hoekstra, A. Y., 2013. The Water Footprint of Modern Consumer Society. Routledge, London.
796
Hoekstra, A. Y., Chapagain, A. K., Aldaya, M. M., 2011. The Water Footprint Assessment Manual. Earthscan, Lon-
797 798
don. Huang, J., Yang, X., Cheng, G., Wang, S., 2014. A comprehensive eco-efficiency model and dynamics of regional
42
ACCEPTED MANUSCRIPT
799
eco-efficiency in China. J. Clean. Prod. 67, 228–238.
800
Huppes, G., Ishikawa, M., 2005. A Framework for Quantified Eco-Efficiency Analysis. J. Ind. Ecol. 9 (4), 25–42.
801
Jiao, X. Q., He, G., Cui, Z. L., Shen, J. B., Zhang, F. S., 2018. Agri-environment policy for grain production in China:
805 806 807 808 809 810 811 812
RI PT
804
Jollands, N., Lermit, J., Patterson, M., 2004. Aggregate eco-efficiency indices for New Zealand - A principal components analysis. J. Environ. Manage. 73 (4), 293–305.
Kim, S., Han, G., 2001. A decomposition of total factor productivity growth in Korean manufacturing industries: A stochastic frontier approach. J. Prod. Anal. 16 (3), 269–281.
Kontodimopoulos, N., Papathanasiou, N. D., Flokou, A., Tountas, Y., Niakas, D., 2011. The impact of nondiscretionary factors on DEA and SFA technical efficiency differences. J. of Med. Syst. 35 (5), 981–989.
SC
803
Toward sustainable intensification. China Agr. Econ. Rev. 10 (1), 78–92.
Koskela, M., 2015. Measuring eco-efficiency in the Finnish forest industry using public data. J. Clean. Prod. 98, 316–327.
M AN U
802
Kumbhakar, S. C., Ghosh, S., McGuckin, J. T., 1991. A Generalized Production Frontier Approach for Estimating Determinnatns of Inefficiency in US Dairy Farms. J. Bus. Econ. Stat. 9 (3), 279–286.
813
Kumbhakar, S. C., Lovell, C. a. K., 2000. Stochastic frontier analysis. Cambridge University Press, Cambridge.
814
Kuosmanen, T., Kortelainen, M., 2005. Measuring eco-efficiency of production with data envelopment analysis. J.
818 819 820 821 822 823 824 825 826 827 828 829 830 831 832
sis. J. Life Cycle Ass. 7 (5), 261–268.
TE D
817
Landsiedel, R., Saling, P., 2002. Assessment of toxicological risks for life cycle assessment and eco-efficiency analy-
Lauwers, L., 2009. Justifying the incorporation of the materials balance principle into frontier-based eco-efficiency models. Ecol. Econ. 68 (6), 1605–1614.
Li, T., Long, H., Zhang, Y., Tu, S., Ge, D., Li, Y., Hu, B., 2017. Analysis of the spatial mismatch of grain production and farmland resources in China based on the potential crop rotation system. Land Use Policy 60, 26–36.
EP
816
Ind. Ecol. 9 (4), 59–72.
Li, D., Zhu, J., Hui, E. C. M., Leung, B. Y. P., Li, Q., 2011. An emergy analysis-based methodology for eco-efficiency evaluation of building manufacturing. Ecol. Indic. 11 (5), 1419–1425. Meeusen, W., van Den Broeck, J., 1977. Efficiency Estimation from Cobb-Douglas Production Functions with Com-
AC C
815
posed Error. Int. Econ. Rev. 18 (2), 435. Mekonnen, M. M., Hoekstra, A. Y., 2011. The green, blue and grey water footprint of crops and derived crop products. Hydrol. Earth Syst. Sc. 15 (5), 1577–1600. Michelsen, O., Fet, A. M., Dahlsrud, A., 2006. Eco-efficiency in extended supply chains: A case study of furniture production. J. Environ. Manage. 79 (3), 290–297. Mickwitz, P., Melanen, M., Rosenstr¨om, U., Sepp¨al¨a, J., 2006. Regional eco-efficiency indicators - a participatory approach. J. Clean. Prod. 14 (18), 1603–1611. M¨uller, K., Holmes, A., Deurer, M., Clothier, B. E., 2015. Eco-efficiency as a sustainability measure for kiwifruit
43
ACCEPTED MANUSCRIPT
833
production in New Zealand. J. Clean. Prod. 106, 333–342.
834
Organization for Economic Co-operation and Development (OECD), 1998. Eco-efficiency. Paris.
835
Park, H. S., Behera, S. K., 2014. Methodological aspects of applying eco-efficiency indicators to industrial symbiosis
837 838
networks. J. Clean. Prod. 64, 478–485. Park, P. J., Tahara, K., Inaba, A., 2007. Product quality-based eco-efficiency applied to digital cameras. J. Environ.
RI PT
836
Manage. 83 (2), 158–170.
839
Park, Y. S., Egilmez, G., Kucukvar, M., 2015. A Novel Life Cycle-based Principal Component Analysis Framework
840
for Eco-efficiency Analysis: Case of the United States Manufacturing and Transportation Nexus. J. Clean. Prod.
841
92, 327–342.
846 847 848 849 850 851 852 853 854 855 856 857 858 859 860 861 862 863 864 865 866
SC
M AN U
845
Price Division of Chinese National Development and Reform Commission, 1998-2016. China agricultural products cost-benefit compilation of information. China Price Press, Beijing.
Quariguasi Frota Neto, J., Walther, G., Bloemhof, J., van Nunen, J. A., Spengler, T., 2009. A methodology for assessing eco-efficiency in logistics networks. Eur. J. Oper. Res. 193 (3), 670–682. Rashidi, K., Farzipoor Saen, R., 2015. Measuring eco-efficiency based on green indicators and potentials in energy saving and undesirable output abatement. Energ. Econ. 50, 18–26.
Reifschneider, D., Stevenson, R., 1991. Systematic Departures from the Frontier: A Framework for the Analysis of Firm Inefficiency. Int. Econ. Rev. 32 (3), 715.
TE D
844
opment Analysis approach. J. Environ. Manage. 92 (4), 1154–1164.
Reinhard, S., Knox Lovell, C. A., Thijssen, G. J., 2000. Environmental efficiency with multiple environmentally detrimental variables; estimated with SFA and DEA. Eur. J. Oper. Res. 121 (2), 287–303. Reith, C. C., Guidry, M. J., 2003. Eco-efficiency analysis of an agricultural research complex. J. Environ. Manage. 68 (3), 219–229.
EP
843
Picazo-Tadeo, A. J., G´omez-Lim´on, J. A., Reig-Mart´ınez, E., 2011. Assessing farming eco-efficiency: A Data Envel-
Robaina-Alves, M., Moutinho, V., MacEdo, P., 2015. A new frontier approach to model the eco-efficiency in European countries. J. Clean. Prod. 103, 562–573.
Sailing, P., Kicherer, A., Dittrich-Kr¨aamer, B., Wittlinger, R., Zombik, W., Schmidt, I., Schrott, W., Schmidt, S., 2002.
AC C
842
Eco-efficiency analisis by BASF: the method. Int. J. Life Cycle Ass. 7 (4), 203–218. ¨ Schaltegger, S., Sturm, A., 1990. Okologische rationalit¨at: ansatzpunkte zur ausgestaltung von o¨ kologieorientierten managementinstrumenten. Die Unternehmung 4, 273–290. Sepp¨al¨aa, J., Melanen, M., M¨aenp¨aa¨ , I., Koskela, S., Tenhunen, J., Hiltunen, M.-R., 2005. How Can the Eco-efficiency of a Region be Measured and Monitored? J. Ind. Ecol. 9 (4), 117–130. Sun, S., Wang, Y., Engel, B. A., Wu, P., 2016. Effects of virtual water flow on regional water resources stress: A case study of grain in China. Sci. Total Environ. 550, 871–879. Thanawong, K., Perret, S. R., Basset-Mens, C., 2014. Eco-efficiency of paddy rice production in Northeastern Thai-
44
ACCEPTED MANUSCRIPT
870 871 872 873 874 875 876 877 878
Van Caneghem, J., Block, C., Cramm, P., Mortier, R., Vandecasteele, C., 2010a. Improving eco-efficiency in the steel industry: The ArcelorMittal Gent case. J. Clean. Prod. 18 (8), 807–814. Van Caneghem, J., Block, C., Van Hooste, H., Vandecasteele, C., 2010b. Eco-efficiency trends of the Flemish industry:
RI PT
869
Van Berkel, R., 2007. Eco-efficiency in the Australian minerals processing sector. J. Clean. Prod. 15 (8-9), 772–781.
Decoupling of environmental impact from economic growth. J. Clean. Prod. 18 (14), 1349–1357.
Wang, J., Zhang, Z., Liu, Y., 2018. Spatial shifts in grain production increases in China and implications for food security. Land Use Policy 74 (April 2017), 204–213.
World Business Council for Sustainable Development (WBCSD), 1992. Eco-efficiency Learning Module. Available at: www.wbcsd.org (access on 6/22/2016).
SC
868
land: A comparison of rain-fed and irrigated cropping systems. J. Clean. Prod. 73, 204–217.
Wursthorn, S., Poganietz, W. R., Schebek, L., 2011. Economic-environmental monitoring indicators for European countries: A disaggregated sector-based approach for monitoring eco-efficiency. Ecol. Econ. 70 (3), 487–496.
M AN U
867
879
Yang, L., Zhang, X., 2018. Assessing regional eco-efficiency from the perspective of resource, environmental and
880
economic performance in China: A bootstrapping approach in global data envelopment analysis. J. Clean. Prod.
881
173, 100–111.
885 886 887 888 889 890 891 892 893
Zhang, B., Bi, J., Fan, Z., Yuan, Z., Ge, J., 2008. Eco-efficiency analysis of industrial system in China: A data envelopment analysis approach. Ecol. Econ. 68 (1-2), 306–316.
TE D
884
pressure from economic growth. Ecol. Indic. 24, 177–184.
Zhang, J., Liu, Y., Chang, Y., Zhang, L., 2017. Industrial eco-efficiency in China: A provincial quantification using three-stage data envelopment analysis. J. Clean. Prod. 143, 238–249. Zhang, Y., Li, Y., Ouyang, Z., Liu, J., 2015. The grey water footprint of the winter wheat-summer maize crop rotation system of the North China Plain. Acta Ecologica Sinica (in Chinese) 35 (20), 6647–6654.
EP
883
Yu, Y., Chen, D., Zhu, B., Hu, S., 2013. Eco-efficiency trends in China, 1978-2010: Decoupling environmental
Zhou, P., Ang, B. W., Zhou, D. Q., 2012. Measuring economy-wide energy efficiency performance: A parametric frontier approach. Appl. Energ. 90 (1), 196–200. Zhu, Z., Wang, K., Zhang, B., 2014. Applying a network data envelopment analysis model to quantify the eco-
AC C
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efficiency of products: A case study of pesticides. J. Clean. Prod. 69, 67–73.
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An integrated WF-SFA method is developed to estimate eco-efficiency and its
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determinants. Provincial water footprint and grain production eco-efficiency in China are
assessed.
Determinants of eco-efficiency and key inputs of ecological grain production are
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analysed.
The change in eco-efficiency and its role in the total factor productivity are
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discussed.