Total-factor ecology efficiency of regions in China

Ecological Indicators 73 (2017) 284–292

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Total-factor ecology efficiency of regions in China Shujing Yue ∗ , Yang Yang, Zhengning Pu School of Economics & Management, Southeast University, Nanjing 211189, China

a r t i c l e

i n f o

Article history: Received 18 February 2016 Received in revised form 20 September 2016 Accepted 28 September 2016 Keywords: Slack-based measure (SBM) Ecological Footprint (EF) Ecological efficiency Total-factor ecology efficiency (TFEcE)

a b s t r a c t This paper combines the concept of Ecological Footprint (EF) with the framework of total-factor energy efficiency to develop a new index of total-factor ecology efficiency (TFEcE), which is constructed as the ratio of the target EF input obtained from SBM (slack-based measure) model to the actual ecology input under the consideration of labor and capital inputs. This paper computes the TFEcE of 28 provinces in China for the period 2000–2012. Findings show that China’s TFEcE remains a low level of 0.5, which urgently needs to be improved. China’s regional TFEcE is extremely unbalanced and the eastern area ranks first with the highest score. Compared with total-factor energy efficiency and traditional singlefactor ecological efficiency, The TFEcE index evaluates ecology efficiency more comprehensively through taking EF in conjunction with the total-factor framework. © 2016 Elsevier Ltd. All rights reserved.

1. Introduction China’s economy has grown aggressively in the past thirty-five years, as its GDP (gross domestic product) has grown by almost 150 times from 1979 to 2014 (National Bureau of Statistics of China, 2015). At the same time, severe ecological problems behind this prosperous scenario are becoming worse: China became the world’s largest contributor of carbon dioxide emissions in 2007 and the largest energy consumer in 2010, China is also facing intensified soil erosion, increasing water pollution, stern grassland degradation, and haze pollution covering most of the land area. China’s total ecological impact will not fall due to continued stable economic growth in the future. Along with this fast demand for ecology input, the efficiency of ecology should be of concern especially under China’s pursuing overall improvement of the ecological environment in the 13th Five-Year Plan (2016–2020). Ecological efficiency means doing more with less, or producing economic outputs with minimal natural resources and environmental degradation (Kuosmanen, 2005). Since first described by Schaltegger and Sturm (1989) and widely publicized in Changing Course (1992), ecological efficiency has been proposed as an effective means to transform unsustainable development to sustainable development (Mickwitz et al., 2006). Although ecological efficiency assessment is a complicated and multidisciplinary task (Zhao et al.,

∗ Corresponding author at: School of Economics & Management, Southeast University, Jiangning District, Nanjing, Jiangsu Province 211189, China. E-mail addresses: yue [email protected] (S. Yue), [email protected] (Y. Yang), [email protected] (Z. Pu). http://dx.doi.org/10.1016/j.ecolind.2016.09.047 1470-160X/© 2016 Elsevier Ltd. All rights reserved.

2006), it is widely measured as the ratio between the added value of a product or service and the ecological impacts of the product or service (Yu et al., 2013). In the empirical study, GDP is often used as the numerator, and consumption of energy (Hu and Wang, 2006), emissions of CO2 (Zhang et al., 2008), domestic extraction used (Yu et al., 2013) or material flow (Wang et al., 2016) is usually placed in the denominator as indicators of ecological pressure. In the above literature, the ecological efficiency is measured in the presence of only specific resource input, neglecting other ecological impacts from humanity. The most comprehensive measure of humanity’s overall impact may be the Ecological Footprint (EF), which is firstly put forward by Rees (1992) and improved by Wackernagel and Rees (1996). Chen et al. (2004) stated that the ratio of GDP to EF can be considered as a measure of the resource efficiency. Fu et al. (2015) develops a new method of calculating the resource efficiency by using the EF as an indicator of the ecological input and GDP as the output. The above studies either use a specific ecology input such as energy, water, or land, or use a comprehensive ecological input such as EF to construct the index of ecological efficiency. All these indices only take ecology into account as input to produce outputs. However, the fact is that ecology alone cannot produce any output. Ecology must be put together with other inputs such as labor, capital stock to produce GDP. Just like the single-factor energy efficiency index has been obtained widespread criticism (Patterson, 1996), single-factor ecological efficiency index in the previous literature would also lead to misleading conclusions. Therefore, a multi-input model considering other inputs in a total-factor framework should be applied to correctly assess the ecological efficiency.

S. Yue et al. / Ecological Indicators 73 (2017) 284–292

In a total-factor framework, Hu and Wang (2006) innovatively built the total-factor energy efficiency (TFEE) index using data envelopment analysis (DEA). Incorporating water as an input as well as using conventional inputs such as labor employment and capital stock, Hu et al. (2006) established an index of water efficiency based on the TFEE. Honma and Hu (2008) computed the regional TFEE in Japan for the period of 1993–2003 and discovered a U-shape relation between energy efficiency and per capita income for the regions in Japan. Zhang and Choi (2013), Zhao et al. (2014) analyzed the changes of TFEE in China at the provincial level considering capital, labor and energy as inputs and value added as output. Li and Hu (2012) initially computed the ecological totalfactor energy efficiency (ETFEE) of 30 provinces in China taking into account undesirable outputs. Zhang et al. (2015) proposed a metafrontier slack-based efficiency measure approach to model ETFEE and empirically analyzed regional ETFEE of China during 2001–2010. Although TFEE and ETFEE measure energy efficiency in a totalfactor framework, both of them only take energy into account as the single ecological input while neglecting other ecological inputs such as cultivated input, forest input, grassland input, productivewater input, and build-up land input. Until now, as far as we know there has been no systematic research on the efficiency of a much wider spectrum of ecological inputs in the total-factor framework. Following the idea of Hu and Wang (2006) proposing the totalfactor energy efficiency, we use EF as the comprehensive proxy of ecology inputs to build a new index of ecology efficiency and have named it the total-factor ecology efficiency (TFEcE),1 which is constructed as the ratio of the target EF input obtained from slack-based measure (SBM) model in a total-factor framework to the actual ecology input. Compared with the traditional singlefactor ecological efficiency (i.e., ecological input/GDP), which only takes ecological input into account as a single input, “Total-factor” in TFEcE index of this paper means not only ecological input, but also capital and labor are taken into consideration as the key input factors to produce GDP. The potential contributions of this paper are as follows. Firstly, different from the paper of Hu and Wang (2006), this paper extends the index of TFEE and replaces the energy in TFEE as EF, which is a more comprehensive index. Also, different from the existing papers relevant to the EF always using EF to evaluate single-factor ecology efficiency while neglecting other key input factors, this paper introduces EF into the total-factor framework, which expands the role of EF and combines it with economic analysis. To sum up, we combine the framework of the total-factor efficiency with EF to develop the new index of TFEcE. Secondly, we calculate the TFEcE of China’s 28 provinces from 2000 to 2012 and clarify the discrepancy of TFEcE among eastern, middle, and western areas of China, Thirdly, we discuss the difference between TFEcE and traditional single-factor ecological efficiency neglecting labor and capital inputs, and we also analyze the difference between TFEcE and TFEE proposed by Hu and Wang (2006). This paper is organized as follows. Section 2 presents our methodology of EF and TFEcE. Section 3 describes the data we used and evaluates the EF of 28 provinces in mainland China from 2000 to 2012. Section 4 provides the empirical study for the ecology efficiency of provinces in China based on TFEcE. Finally, Section 5 concludes this paper.

1 The definition of “total-factor” is consistent with Hu and Wang (2006), who firstly proposed the concept of total-factor energy efficiency. Total-factor energy efficiency in their paper considers not only the energy input, but also capital stock and labor employment inputs.

285

2. Methodology In this section, we first calculate EF as the comprehensive measurement of ecological resources occupied by humanity, then consider the EF as ecological input and introduce it into the SBM model, to calculate the TFEcE in a total-factor framework. 2.1. Ecological Footprint EF is a simple evaluation method for sustainable development from the perspective of the total areas of productive land and water required to produce all the resources consumed and to assimilate all the wastes produced (WWF, 2006). The EF methodology converts the regional resource and energy consumption into a variety of biologically productive areas. In this paper, the calculation of EF from 2000 to 2012 for China’s 28 provinces is mainly based on the compound approach put forward by Wackernagel (Wackernagel and Rees, 1996; Wackernagel et al., 1999). EF is the sum of its six types ecologically production land, i.e., arable land, forest land, pasture land, water land, fossil energy land, build-up land. The computational formula for EF is as follows: EF =

P i

∗ YFi ∗ EQFi

YPi

(1)

In the formula above, EF is the total ecological footprint; i is the type of area of the biological productive land required; Pi is the consumption of ith type of resources by a certain human population; YPi is the average productivity of producing ith type of resources in a certain productive area; YFi is the yield factor of ith land type; EQFi is the equivalence factor of ith land type. 2.2. SBM model Built upon the basic CCR-DEA (Charnes et al., 1978) model, Tone (2001) proposed the SBM to measure efficiency based on input excesses and output shortfalls. Being a non-radial approach, SBM overcomes the conventional radial DEA method’s overestimatinglimitation which is caused by neglecting slack variables (Fukuyama and Weber, 2009). Furthermore, SBM directly accounts for input and output slacks in efficiency measurements, with the advantage of capturing the whole aspect of inefficiency (Zhang et al., 2015). Assume that there are n = 1, . . .. . ., N provinces in China, and each province uses input vector x ∈ Rm + to jointly produce output vector y ∈ Rs+ . In this paper, the output vector contains provincial GDP. The input vector contains capital stock, labor, and EF. The fractional programming problem of the constant-returns to scale SBM model is expressed as follows: Minimize

␳=

 

 m − s /xio  s i=1 +i

1 − 1/m 1 + 1/s

s r=1 r

/yro

xo = X␭ + s− , Subjected to

yo = Y ␭ − s+ ,  ≥ 0, s− ≥ 0, s+ ≥ 0.

(2)

, s+ Where s− r , xo and yo represent the input slack, output slack, input i and output for the oth province, respectively; S− , S+ , X and Y are the corresponding matrices of the input slack, output slack, input and output; ␭ is a nonnegative multiplier vector. ␳ is the overall efficiency score for the oth province. If ␳ = 1 (which indicates that all the slack variables are 0), the oth province is SBM-efficient. If the slack of EF is 0, the oth province is ecological efficient.

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2.3. Total-factor ecology efficiency (TFEcE) Following the thought of Hu and Wang (2006), using the EF to replace the energy input in the paper of Hu and Wang (2006), we define the TFEcE for oth province at time t as the ratio of target ecology input to actual ecology input, as seen below. TFEcE (o, t) =

Target ecology input (o, t) Actual ecology input (o, t)

(3)

The target ecology input for each province can be calculated from SBM model, and the target ecology input is defined as: Target ecology input = Actual ecology input (o, t) − Ecology input slack (o, t)

(4) s− i

Where ecological input slack can be obtained from in model (2), which includes labor input slack, capital input slack and ecological input slack. As the target ecology input is the best practical minimum level of ecology input in a province, the actual ecology input is therefore always larger than or equal to this target ecology input. Therefore TFEcE lies always between zero and unity. If the target ecology input is equal to the actual level, then the TFEcE is unity, indicating the best TFEcE on the technology frontier. 3. Data source and evaluation of EF 3.1. Data and material Because of seriously absence of data, Tibet, Hainan, and Chongqing are excluded from this paper. The dataset is compiled for 28 provinces in mainland China from 2000 to 2012. The EF index is calculated by considering the biological materials and the biological wastes. These materials and wastes each demand ecologically productive areas. In this paper, agricultural products mainly include grain, oil, vegetables, tea, etc. Animal products include beef, mutton and milk. Forest products mainly include fruit and wood. Aquatic products include freshwater and marine products. For fossil energy, we mainly consider coal, coke, crude oil, natural gas and other energy. For build-up land, we use the sum of cities’ build-up land areas in a province as the build-up land. Table 1 shows more details. The data used in the calculation of EF are mainly collected from the above 28 provinces Statistical Yearbooks, China Statistical Yearbooks, China Energy Statistical Yearbooks, and the FAO database provided by the Food and Agriculture Organization of the United Nations. We adopt the value of the yield factors and equivalence factors from relevant literature (Xie et al., 2008; Lin et al., 2016). In the SBM model, we employ EF, capital stock and labor as inputs, and GDP as output. EF is obtained by the method mentioned above. Capital stock is not available in any China statistical data. We calculate the capital stock from 2000 to 2012 with 2000 prices using the perpetual inventory method. The labor is employed in terms of labor force. The real GDP is transformed into 2000 prices with a GDP deflator. All the data for labor, capital formation, capital price indices, GDP and GDP deflator come from China Statistical Yearbook. 3.2. Evaluation of EF in China from 2000 to 2012 Fig. 1 shows China’s total EF trend from 2000 to 2012. Firstly, the total EF increased by 143% from 1.76 billion hm2 in 2000 to 4.29 billion hm2 in 2012, took on an ascending trend but increased slowly after 2007. Secondly, the growth rate of EF before 2006 was fast, while slowed down after 2007, mainly because China has especially paid attention to ecological protection in recent years.

The relative proportions of six types footprint are shown in Fig. 1. The energy footprint dominated by fossil fuels, was the fastest growing component of China’s EF from 2000 to 2012, and increased by nearly 198% from 35.3 million hm2 in 2000 to 105.14 million hm2 in 2012. The biological EF, which includes agricultural products, animal products, forest products, and aquatic products, accounted for more than one third of the total EF, but its share was reduced from 38.03% in 2000 to 24.35% in 2012. The proportion of China’s built-up EF is small, but has an upward trend. Biological EF can be divided into arable land EF, pasture land EF, forest land EF and fisheries land EF. The proportion of arable land EF decreased from 65.83% in 2000 to 56.53% in 2012, but still occupied a large proportion of biological EF. From the perspective of economic development and geographical location, previous scholars usually divide China’s provinces into three major areas: the eastern, central and western region. The eastern region constitutes 9 provinces, including Beijing, Tianjin, Shanghai, Liaoning, Jiangsu, Zhejiang, Fujian, Shandong, and Guangdong. This is a coastal region, and the most developed area in China. The central region consists of 10 provinces, including Hebei, Shanxi, Inner Mongolia, Jilin, Heilongjiang, Anhui, Jiangxi, Henan, Hubei, and Hunan. The western region covers 9 provinces, including Guangxi, Sichuan, Guizhou, Yunnan, Shaanxi, Gansu, Qinghai, Ningxia, and Xinjiang. Compared with the other two regions, the western region is the most undeveloped region in China. There are big differences between the EF of each province as Table 2 presents. Shandong’s EF is the highest, while Qinghai’s EF is the lowest, with only 13.67 million hm2 on average from 2000 to 2012, less than 274.55 million hm2 of Shandong’s. The average EF of western region is 62.87 million hm2 , which is much lower than the 116.21 million hm2 of the eastern region and 135.41 million hm2 of the central region during the sample years. 4. Empirical analysis Different from the paper of Hu and Wang (2006), this paper replaces the energy in TEFF as EF (the sum of six ecological lands), and different from the existing papers using EF to evaluate the traditional single-factor ecology efficiency, this paper adds labor and capital factors to assess the ecological efficiency in a total-factor framework. Therefore, the following analysis is made up of four parts. The first part calculates the new TFEcE index, and then the second part presents the contribution of six ecological lands to the TFEcE. The third part analyzes the effects of labor and capital to TFEcE to identify the necessity of adding labor and capital inputs to assess ecological efficiency. The fourth part compares the TFEE in Hu and Wang (2006) and TFEcE in this paper to identify the necessity of considering EF as the comprehensive ecological inputs. 4.1. Analysis of China’s TFEcE Based on the above model in Section 2, we calculate the TFEcE of China, as Table 3 presents. On average, China’s provincial TFEcE is at a low level of about 0.50 during 2000–2012, which indicates the actual ecology input could be reduced by 50%, with GDP unchanged, through ecology efficiency improvement. This means that China’s TFEcE has a large space to improve, and improving the TFEcE is one of the effective ways to solve the dilemma faced by China between rapid economic growth and the increasing pressure on ecological protection. On the whole, the TFEcE of China has achieved a slowly growth, from 0.52 in 2000 to 0.56 in 2012. The TFEcE in 2007 was the lowest, at 0.46, and it has realized a relatively steady increase since 2007. The Chinese government has become more aware of ecological problems since around the year 2007, and it proposed

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Table 1 Description of land type. Land type

Main purpose

Items

Balance factor

Arable land

Crops in the planting industry

2.19

Grassland

Improving animal by-product

Forestland Water land Fossil energy land

Improving forest products and wood Providing aquatic products Absorbing CO2 released by humans

Build-up land

Land for human settlement

Grain (10.89%), Oil (0.90%), Vegetables (2.55%), Tea (0.05%), Pork (1.66%), Beef (0.05%), Mutton (0.02%), Milk (0.11%), Egg (0.68%) Beef (2.82%), Mutton (2.80%), Milk (3.48%) Fruit (0.76%), Wood (2.15%) Aquatic products (0.40%) Coal (49.84%), Coke (6.03%), Crude oil (6.90%), Gasoline (1.47%) Kerosene (0.27%), Diesel oil (2.43%) Fuel oil (1.10%), Natural gas (1.35%) Build-up land (1.29%)

0.48 1.4 0.36 1.4

2.19

Notes: (1) The percentage in brackets indicates the contribution of every item to EF. (2) Balance factors represent the world average productivity of a given ecological land relative to the world average productivity of all ecological lands. Various ecological lands, representing large differences in biological productivity, can be aggregated after adjusted by balance factors.

Ecological footprint/million hm2 5000 4500 4000 3500 3000 2500 2000 1500 1000 500 0 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 Arable land

Pasture land

Forestland

Water land

Fossil energy land

Build-up land

Fig. 1. Time series of total EF components in China 2000–2012.

the binding index of energy saving and emissions mitigation in 2006 and 2009 respectively. The Chinese government also released China’s national program to address climate change, which was the first corresponding program in developing countries. These policies may be helpful to the TFEcE improvement. The regional TFEcE of China is very imbalanced. The eastern region has the highest TFEcE, where the average TFEcE score is 0.76, almost double that of the central region. Beijing, Shanghai and Guangdong, which are all located in the eastern region, always perform the best in terms of TFEcE during the research period. In more detailed terms, Beijing, the cultural center of China, and Shanghai, the economic center of China, have developed service and technology-intensive industries, with the characteristics of low resource consumption and high economic output. Guangdong is the province with the highest GDP, and is also at the forefront of China’s opening up. The large influx of FDI and high foreign trade ratio enable Guangdong to obtain international advanced technology and positive spillover of management experience, which are beneficial to improve the TFEcE score. Compared to the eastern region, the central and western regions obviously have low TFEcE. The central region has the worst TFEcE ranking, and the TFEcE of central and western regions are 0.36 and 0.40 respectively during 2000–2012. The two provinces with the lowest TFEcE are Shanxi in the central region, and Xinjiang in the western region, where the TFEcE is 0.18 and 0.25 respectively. Shanxi is a typical province having abundant coal resource. The out-

put of raw coal in Shanxi accounts for more than 20% of China’s total. The main industries in Shanxi include coal mining and dressing, smelting and pressing of ferrous metals, and petroleum processing, which are all energy-intensive industries, occupying 57.59%, 7.86%, 4.07% of industry’s total value-added respectively. Too much consumption of energy input contributes to the low TFEcE in Shanxi. Different from Shanxi, Xinjiang’s energy-intensive sector is not the main industry. The amount of energy consumption ranks only 20th during 2000–2012 among 28 provinces in descending order, but Xinjiang’s high biological EF ranking 13th leads to a high level of total EF, which causes Xinjiang’s low TFEcE. 4.2. Contribution of six ecological lands to TFEcE2 In this section, we take six ecological lands as different ecological inputs to analyze the contribution of six ecological lands to TFEcE’.3 The input factors in the model (2) are now as follows: arable land input, forest land input, pasture land input, water land input, fossil energy land input, build-up land input, total labor input, and total

2 We really appreciate the reviewer’s suggestion about the analysis of the contribution of six ecological lands to TFEcE. 3 TFEcE takes total EF in conjunction with labor and capital stock as inputs, while TFEcE’ takes six different ecological lands in conjunction with labor and capital stock as inputs. There are three inputs and one output to assess the index of TFEcE, while the index of TFEcE’ uses eight inputs and one output.

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Table 2 EF by region from 2000 to 2012 (million ha). Region

2000

2002

2004

2006

2008

2010

2012

Beijing Tianjin Hebei Shanxi Inner Mongolia Liaoning Jilin Heilongjiang Shanghai Jiangsu Zhejiang Anhui Fujian Jiangxi Shandong Henan Hubei Hunan Guangdong Guangxi Sichuan Guizhou Yunnan Shaanxi Gansu Qinghai Ningxia Xinjiang Average East Central West

30.842 25.929 131.296 99.739 64.309 110.354 55.295 88.680 48.348 97.973 58.187 74.938 32.973 37.453 135.035 120.791 73.746 60.392 86.522 37.139 77.369 43.252 37.276 36.342 32.064 8.109 11.213 47.151 62.954 69.574 80.664 36.657

32.685 29.917 148.588 132.003 74.712 116.657 61.763 93.457 50.542 109.318 64.891 81.831 37.668 39.232 156.426 136.155 75.695 66.483 92.755 38.582 82.690 43.309 44.083 43.096 35.600 9.528 5.387 52.161 69.829 76.762 90.992 39.382

37.704 36.552 189.846 157.131 125.188 140.584 73.228 110.645 58.213 137.990 83.602 91.375 48.066 51.873 213.788 171.888 88.003 82.234 115.543 48.882 104.393 60.522 33.029 58.950 44.436 10.882 24.427 64.422 87.978 96.893 114.141 49.994

38.522 41.101 241.737 188.301 179.152 170.699 90.283 128.880 61.792 179.517 105.460 100.667 59.484 59.892 292.535 224.301 104.195 107.573 137.821 58.955 112.176 74.604 79.947 77.396 50.710 13.180 29.993 81.382 110.366 120.770 142.498 64.260

40.040 42.714 262.875 188.609 223.223 186.047 94.571 146.451 66.106 193.464 118.035 114.441 66.916 65.949 334.648 239.938 112.217 115.033 152.332 66.183 130.838 74.222 84.568 91.302 56.697 16.130 36.651 84.491 121.596 133.367 156.331 71.231

40.907 53.512 289.474 200.687 252.879 208.612 104.716 164.649 71.456 220.429 128.158 129.095 79.695 71.951 375.128 259.766 133.246 122.427 178.403 80.882 144.543 80.327 95.708 112.336 62.102 16.725 45.749 104.981 136.734 150.700 172.889 82.595

39.055 58.598 325.182 231.158 305.386 230.037 116.239 178.863 72.384 256.218 131.427 141.888 87.666 78.124 411.809 267.675 149.941 131.065 193.070 101.055 151.401 95.060 103.388 138.766 72.509 21.731 62.170 133.615 153.053 164.474 192.552 97.744

Table 3 TFEcE and TFEE by region (2000–2012). Total-factor ecology efficiency

Total-factor energy efficiency

Difference between TFEcE and TFEE

Region

2000

2008

2012

2000

2008

2012

2000

2008

2012

Beijing Tianjin Hebei Shanxi Inner Mongolia Liaoning Jilin Heilongjiang Shanghai Jiangsu Zhejiang Anhui Fujian Jiangxi Shandong Henan Hubei Hunan Guangdong Guangxi Sichuan Guizhou Yunnan Shaanxi Gansu Qinghai Ningxia Xinjiang Average East Central West

1 0.664 0.342 0.158 0.201 0.356 0.302 0.3 1 0.729 0.854 0.35 0.92 0.436 0.52 0.338 1 0.478 1 0.457 0.444 0.297 0.459 0.472 0.273 0.605 0.397 0.306 0.523 0.783 0.39 0.412

1 0.756 0.325 0.176 0.142 0.38 0.354 0.271 1 0.696 0.79 0.354 0.775 0.452 0.404 0.375 0.439 0.404 1 0.437 0.448 0.374 0.351 0.371 0.26 0.453 0.323 0.239 0.477 0.756 0.329 0.362

1 0.686 0.304 0.227 0.547 0.357 0.353 0.304 1 0.654 0.815 0.372 0.989 0.499 0.415 0.77 0.787 0.494 1 0.605 0.512 0.62 0.489 0.343 0.635 0.376 0.303 0.196 0.559 0.768 0.466 0.453

0.855 0.701 0.44 0.254 0.346 0.426 0.503 0.508 1 0.901 0.845 0.595 1 0.711 0.691 0.592 1 0.775 1 0.694 0.556 0.302 0.573 0.619 0.312 0.573 0.453 0.382 0.629 0.824 0.572 0.496

1 0.818 0.519 0.314 0.305 0.601 0.712 0.74 1 0.931 0.941 0.69 0.98 0.761 0.706 0.619 0.675 0.565 1 0.666 0.676 0.364 0.556 0.619 0.404 0.381 0.347 0.427 0.654 0.886 0.59 0.493

1 0.773 0.54 0.325 0.391 0.958 0.68 0.855 1 0.877 0.894 0.812 0.927 0.931 0.685 0.999 0.62 0.622 1 0.963 0.687 0.396 0.504 0.61 0.776 0.3 0.382 0.328 0.708 0.901 0.677 0.55

0.145 −0.037 −0.098 −0.096 −0.145 −0.07 −0.201 −0.208 0 −0.172 0.009 −0.245 −0.08 −0.275 −0.171 −0.254 0 −0.297 0 −0.237 −0.112 −0.005 −0.114 −0.147 −0.039 0.032 −0.056 −0.076 −0.106 −0.041 −0.182 −0.084

0 −0.062 −0.194 −0.138 −0.163 −0.221 −0.358 −0.469 0 −0.235 −0.151 −0.336 −0.205 −0.309 −0.302 −0.244 −0.236 −0.161 0 −0.229 −0.228 0.01 −0.205 −0.248 −0.144 0.072 −0.024 −0.188 −0.177 −0.13 −0.261 −0.131

0 −0.087 −0.236 −0.098 0.156 −0.601 −0.327 −0.551 0 −0.223 −0.079 −0.44 0.062 −0.432 −0.27 −0.229 0.167 −0.128 0 −0.358 −0.175 0.224 −0.015 −0.267 −0.141 0.076 −0.079 −0.132 −0.149 −0.133 −0.211 −0.097

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capital input. The contributions of six types land to TFEcE’ can be calculated as Eqs. (5)–(7). Considering six different ecological land inputs, capital input and labor input as multi-inputs, and GDP as output, TFEcE’ for region o at time t is as follows:

forestland and water land in Fujian make relatively greater contributions to TFEcE’. Finally, the build-up land contribution of Beijing accounts for 4.45%, the largest among 28 provinces. As the capital of China, high level of urbanization and the agglomeration effect jointly lead to an increasing demand for build-up land.

TFEc’ (o, t) =

4.3. Effects of labor and capital on TFEcE4

Rio (n, t) =

 Target ecological land type input (n, t) n Actual ecological land type input (n, t) n

Target ecological land type input (n, t)



n

Contri (n, t) =

Actual ecological land type input (n, t)

Rio t)  (n, n

Rio (n, t)

(5)

(6)

(7)

Where each region has n ecological lands, and n = 1–6, referring to arable land, forest land, pasture land, water land, fossil energy land, and build-up land, respectively. Rio is the ratio of a specific target ecological land input to all actual ecological land inputs, indicating the contribution of specific ecological land to TFEcE’, and Contri is the contribution in percentage form. We calculate TFEcE’ and analyze the contributions of different ecological lands to TFEcE’. The contributions of different land types to national TFEcE’ are shown in Fig. 2. Among the six land types, fossil energy land made the greatest contribution to TFEcE’, accounting for 79.7%, which increased rapidly from 75.02% in 2004 to 84.24% in 2006, then grew slowly and even declined after 2010. Arable land ranked second and made up a proportion of 9.8%, which fell sharply from 17.95% in 2000 to 6.41% in 2006, then tended a slowly rising after 2007. The average contribution of grassland to TFEcE’ was about 6.34%, which increased slowly before 2004 and significantly decreased from 2004 to 2006, then remained stable. Forestland, water land and build-up land accounted a small percentage of TFEcE’, and their average levels were less than 2%, among which water land accounted for the smallest proportion, only 0.35%. From an overall perspective, fossil energy land makes up the largest proportion and water land has the smallest proportion, while the contributions of six ecological lands to TFEcE’ at provincial level are different, and the results are shown in the following Table 4. Henan’s arable land contribution to its TFEcE’ accounts for the largest proportion, 19.44%, among 28 provinces. This may be explained that Henan is the biggest agricultural province in China, and arable land occupies a large proportion of land resources, therefore the contribution of arable land to TFEcE’ is larger than that of other provinces. The contribution of arable land to TFEcE’ in Shanghai, 2.39%, is the lowest, and the value is about 1/7, as much as that of Henan. While the fossil energy land contribution in Shanghai accounts for the largest score of 93%. This is probably determined by the industrial structure and consumption level of Shanghai. Secondary industry, with the characteristic of high energy consumption, is about one-third of the total output in Shanghai. What’s more, as the economic and financial center of China, Shanghai offers almost the highest level of income which causes higher amount of energy consumption. When it comes to the contribution of grassland, Guangdong gains the smallest score, 1.11%, While Qinghai obtains the largest score, 31.33%. Qinghai, one of the major pastoral areas in China, is the largest area in the world of yak and Tibetan sheep and the animal husbandry has become its dominant industry. Therefore the contribution of grassland to TFEcE’ in Qinghai is greater than that in other provinces. While in terms of the contributions of forestland and water land, Fujian has the largest proportion among the 28 provinces, which are respectively 11.19% and 2.25%. The main reason lies in the geographical features of Fujian, which is near the mountain and by the sea. Furthermore, the forest coverage rate reaches 65.95%, ranking first in China and the length of the coastline ranks second. So the

The essential difference between TFEcE and single-factor ecological efficiency is whether to incorporate the labor input and capital input. In this section, we study the effects of labor and capital inputs on the TFEcE of China. The impacts of labor and capital on TFEcE cannot be calculated in a quantitative way under the total-factor framework in our paper. A rough calculation to identify the necessity of adding labor and capital inputs is taken as follows: First, TFEcE1 is assessed only taking labor and EF into account as inputs to produce GDP while ignoring the capital input; Second, TFEcE2 is evaluated only regarding capital and EF as inputs to produce GDP while neglecting the labor input; Finally, TFEcE3 is calculated only taking EF into account as input to produce GDP. Through analyzing the respective differences between original TFEcE and TFEcE1, TFEcE2, TFEcE3, we can carry out a rough analysis on the different impacts of capital and labor on TFEcE. Based on models (2)–(4), we calculate TFEcE1, TFEcE2 and TFEcE3 of China during 2000–2012. The average national level of TFEcE3 and TFEcE1 are separately 0.625 and 0.543, which are significantly greater than the average level of TFEcE, which is 0.50, and the TFEcE3 is the highest among all the indicators. The reason for TFEcE1 being higher than TFEcE is that there may exist a phenomenon of capital input substituting for ecological input, and the ecological efficiency may be overestimated without considering capital input. The highest place of TFEcE3 among these indexes is because not only capital input, but also labor input may substitute for ecological input, therefore the ecological efficiency may be overestimated without considering capital and labor inputs. In order to analyze the different influence of capital and labor on TFEcE, Mann-Whitney U test is conducted, and the results are showed in Table 5. The results of Table 5 show that, the null hypothesis that TFEcE1 (ignoring capital input) and TFEcE has no significant difference can be rejected, indicating that capital has a significant impact on TFEcE. The null hypothesis that TFEcE3 (neglecting labor and capital inputs) and TFEcE has no significant difference can also be rejected, that is to say, TFEcE3 presents significant differences with TFEcE, which means capital and labor have significant impacts on TFEcE. While the null hypothesis that TFEcE2 (ignoring labor input) and TFEcE has no significant difference cannot be rejected, which implies that labor has no significant impact on TFEcE. To sum up, the consideration of labor and capital inputs has a significant influence on TFEcE and the effect of capital on TFEcE is relatively greater than that of labor.5 4.4. Effect of EF on TFEcE The essential difference between total-factor energy efficiency (TFEE) proposed by Hu and Wang (2006) and TFEcE in this paper is whether to incorporate the whole ecological impacts. The TFEcE index is based on the viewpoint of EF and considers not only the energy input, but also the arable land input, pasture land input, forestland input, etc. In this section, we present the comparison of TFEE and TFEcE of 28 provinces in China from 2000 to 2012.

4

We are appreciated the suggestions of the reviewers. Without a quantitatively calculation, we draw this conclusion that the effect of capital on TFEcE is relatively greater than that of labor with caution. 5

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90 80 70 60 50 40 30 20 10 0 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 Arable land

Grassland

Forestland

Water land

Fossil energy land

Build-up land

Fig. 2. The contribution of different land types on national average TFEcE’.

Table 4 The contribution of six land types to TFEcE’ (%).

Beijing Tianjing Hebei Shanxi Inner Mongolia Liaoning Jilin Heilongjiang Shanghai Jiangsu Zhejiang Anhui Fujian Jiangxi Shandong Henan Hubei Hunan Guangdong Guangxi Sichuan Guizhou Yunnan Shanxi Gansu Qinghai Ningxia Xingjiang

Arable land

Grassland

Forestland

Water land

Fossil energy land

Build-up land

4.763 3.308 10.177 5.650 8.018 8.830 14.413 17.369 2.925 13.584 7.960 10.224 14.653 10.176 8.228 19.439 8.838 11.041 13.043 10.346 6.584 6.719 15.471 4.941 16.523 9.223 5.780 6.404

9.187 2.080 5.870 3.538 12.402 2.980 6.335 13.522 1.912 3.322 1.738 2.612 2.472 1.947 2.786 10.414 1.917 2.645 1.113 1.868 3.245 2.751 6.289 9.631 11.484 31.329 7.951 13.627

0.593 0.247 0.856 0.376 1.833 1.172 3.255 4.761 0.200 0.626 1.927 1.953 11.187 2.298 1.255 1.429 1.249 1.880 4.249 4.456 0.499 0.759 2.821 0.467 0.953 0.191 0.371 0.639

0.044 0.153 0.198 0.007 0.052 0.553 0.073 0.096 0.131 0.429 0.822 0.435 2.251 0.551 0.487 0.084 0.500 0.476 1.296 0.738 0.138 0.098 0.120 0.057 0.007 0.003 0.094 0.076

80.965 92.359 81.311 89.640 75.576 84.496 74.309 62.750 92.998 80.277 85.604 82.802 67.904 83.021 85.337 67.335 85.445 81.871 77.317 80.368 87.711 88.157 73.944 81.586 69.490 58.515 84.367 77.768

4.447 1.852 1.587 0.790 2.118 1.970 1.615 1.502 1.833 1.763 1.949 1.975 1.533 2.007 1.906 1.299 2.051 2.088 2.982 2.224 1.822 1.517 1.355 3.318 1.544 0.739 1.437 1.487

Table 5 Significance test between TFEcE1 (TFEcE2, TFEcE3) and TFEcE.

Mann-Whitney U Wilcoxon W Z-value P-value

TFEcE1 vs. TFEcE

TFEcE2 vs. TFEcE

TFEcE3 vs. TFEcE

56777.000 123207.000 −3.341 <0.001

63916.000 130346.000 −0.822 0.411

47482.000 113912.000 −6.616 <0.001

Notes: Mann-Whitney U test is a nonparametric test of the null hypothesis that the distribution of the two population which the two independent samples come from has no significant difference. Wilcoxon W is one of outputs of Mann-Whitney U test, which refers to the rank sum of Mann-Whitney U test.

Table 6 Significance test between TFEcE and TFEE in China.

TFEcE vs. TFEE

Mann-Whitney U

Wilcoxon W

Z-value

P-value

40578.500

107008.500

−9.054

<0.001

Without incorporating other ecological impacts, TFEE may overestimate the province’s performance. As Table 3 shows, the average TFEcE is always lower than the average TFEE. During 2000–2012,

the average TFEE in China is 0.65, while the average TFEcE is 0.50. We use the Mann–Whitney U rank test to discover the statistical difference between TFEcE and TFEE. As Table 6 shows, the

S. Yue et al. / Ecological Indicators 73 (2017) 284–292

difference has a statistical significance with a P-value less than 0.001, which means that the consideration of EF as comprehensive ecological inputs has a significant influence on provincial ecology performance. Since EF mainly consists of biological EF and energy EF, the regions landing on the TFEcE frontier must perform well, with both the dimension of biological EF and the dimension of energy EF, while the regions landing on the TFEE frontier are best with only one dimension of energy savings. Table 3 presents the difference between TFEE and TFEE at the provincial level. As TFEE is always higher than TFEcE, we firstly figure out the average TFEE and TFEE from 2000 to 2012 in 28 provinces. Then, they are ranked in descending order according to the TFEE (or TFEcE) level and we study the differences between TFEcE and TFEE ranks. We can divide the 28 provinces into three groups according to the gap between the TFEcE and TFEE ranks. The first group covers Shanghai and Guangdong. There are no gaps between TFEcE and TFEE for these two provinces, because they always stand on the efficient frontier and rank first for both TFEcE and TFEE. This group keeps ahead of other provinces in both the dimension of biological EF and of energy EF. The second group includes Xinjiang, Heilongjiang, Jilin, Henan, Shandong, Anhui, Jiangxi, etc. Their ranks in TFEcE are lower than that in TFEE. In this group, the biological EF is relatively high, which causes these provinces’ ranks to decrease in TFEcE with the consideration of biological EF. Take Xinjiang as an example, the biological EF, not the energy EF, dominates the total EF, which makes the TFEcE in Xinjiang to be lower than the TFEE in Xinjiang. The third group consists of Beijing, Zhejiang, Sichuan, Guizhou, Yunnan, and Qinghai, whose rank in TFEcE are higher than that in TFEE. These regions have paid more attention to biological EF input and their ability to manage the biological EF input is ignored by the TFEE. Take Zhejiang as an example, the energy EF of Zhejiang ranks 9th among 28 provinces in descending order, but the biological EF ranks 20th in descending order. The good performance in biological EF contributes to the higher TFEcE of Zhejiang. From what we discussed above, compared to TFEE, TFEcE evaluates ecology efficiency more comprehensively: TFEcE not only considers the energy input, but also takes biological footprint into account in the total-factor framework. A province performs well on TFEcE only if the energy input and biological footprint are both efficient.

biological footprint accounts for more than one third of the total EF. On average, China’s TFEcE from 2000 to 2012 is found to be at a low level of almost 0.50, which shows that there is a large space for China’s ecological efficiency to improve. The regional TFEcE of China is unbalanced: the east area gets the highest TFEcE of about 0.76; the west areas ranks second with the TFEcE of 0.40; the central area falls behind with the lowest TFEcE of 0.35. Without taking into account other key inputs such as labor employment and capital stock, traditional single-factor ecological efficiency may lead to misleading conclusions. Neglecting labor and capital inputs has a significant impact on TFEcE. Without taking into account other ecological impacts, TFEE may overestimate the province’s performance, and TFEcE is significantly lower than TFEE. Some provinces such as Beijing and Zhejiang, obtain higher rank in TFEcE due to good performance in biological footprint. Some provinces such as Xinjiang and Heilongjiang, get lower rank in TFEcE caused by too much consumption of biological footprint input. Acknowledgements We are grateful for the financial support provided by the National Natural Science Foundation of China (71303042); the Major project of National Social Science Foundation of China (12&ZD207); and the Key Project of Philosophy and Social Science Research in Colleges and Universities of Jiangsu Province (2015ZDIXM004). Appendix A. See Table A1. Table A1 The abbreviation used in this paper. GDP EF SBM DEA TFEcE TFEcE1 TFEcE2

5. Concluding remarks Based on the viewpoint of EF, we built the TFEcE index, which is a new index by taking the ratio of target ecology input from an SBM model to the actual ecology input. The TFEcE combines the total-factor framework with the concept of EF, taking EF with capital stock and labor employment as multi-inputs. The difference between TFEcE and traditional single-factor ecological efficiency is that the latter only takes ecological input into account while ignoring other key inputs such as capital and labor. The difference between TFEcE and TFEE is that the latter only considers energy as ecological input while neglecting other ecological inputs such as land and water. Therefore, the TFEcE index evaluates ecology efficiency comprehensively through taking EF in conjunction with total-factor framework. Using the TFEcE index, this paper studies 28 provinces’ ecology efficiency in China from 2000 to 2012. The main conclusions are as follows: The total EF increased by 143% from 1.76 billion hm2 in 2000 to 4.29 billion hm2 in 2012. The growth of total EF before 2006 is fast, then slowed down after 2007. The total EF in China is mainly consisted of the energy footprint and biological footprint, and the

291

TFEcE3 TFEcE’ TFEE ETFEE

Gross domestic product Ecological footprint Slack-based measure Data envelopment analysis Total-factor ecological efficiency (inputs: EF, capital, labor; outputs: GDP) Total-factor ecological efficiency 1 (inputs: EF, labor; outputs: GDP) Total-factor ecological efficiency 2 (inputs: EF, capital; outputs: GDP) Total-factor ecological efficiency 3 (inputs: EF; outputs: GDP) Total-factor ecological efficiency’ (inputs: six ecological lands, capital,labor; outputs: GDP) Total-factor energy efficiency (inputs: energy, capital, labor; outputs: GDP) Ecological total-factor energy efficiency (inputs: energy, capital, labor; outputs: GDP,CO2 ,SO2 )

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