Measuring China's regional inclusive green growth

Journal Pre-proof Measuring China's regional inclusive green growth

Yuhuan Sun, Wangwang Ding, Zhiyu Yang, Guangchun Yang, Juntao Du PII:

S0048-9697(19)36363-6

DOI:

https://doi.org/10.1016/j.scitotenv.2019.136367

Reference:

STOTEN 136367

To appear in:

Science of the Total Environment

Received date:

19 October 2019

Revised date:

12 December 2019

Accepted date:

25 December 2019

Please cite this article as: Y. Sun, W. Ding, Z. Yang, et al., Measuring China's regional inclusive green growth, Science of the Total Environment (2020), https://doi.org/10.1016/ j.scitotenv.2019.136367

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Running head: MEASURING CHINA’S REGIONAL INCLUSIVE GREEN GROWTH

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Measuring China’s Regional Inclusive Green Growth Yuhuan Sun a,c, Wangwang Dingb,d, Zhiyu Yang a,e Guangchun Yangb,f, Juntao Du b,g*,1

PR China

-p

School of Statistics of Dongbei University of Finance and Economics, 116025 Dalian, Liaoning,

[email protected]

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c

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PR China

[email protected]

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d

e

[email protected] f

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b

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School of Statistics of Lanzhou University of Finance and Economics, 730101 Lanzhou, Gansu,

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a

[email protected]

g.

1

[email protected]

Corresponding author. Tel.: +86 188 9568 2910. Fax: +86 411 8471 1892.

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Measuring China’s Regional Inclusive Green Growth Abstract: In response to the increasing pressure of global resource management and environmental issues and a slowdown in the related economic growth, China has proposed an inclusive green growth strategy based on coordination between society,

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the economy, and the environment. The alignment of resources with the

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socio-economic development goals is a key issue that must be addressed for inclusive

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green growth. A comprehensive directional distance function and slacks-based

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measure model are proposed to evaluate the inclusive green growth levels of 285

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cities in China from 2003 to 2015. The Luenberger indicator is used to decompose the drivers of inclusive green growth. Our research shows that the main obstacle to

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China‟s inclusive green growth is the magnitude of technical change, which is not

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aligned with China‟s green development level. Hence, it is necessary to coordinate overall inclusive green growth levels using both technical and regional aspects. This research provides a reference not only for China‟s economic green development, but also for that of developing countries, enabling the coordination of economic development and environmental resource protection. Keywords: Inclusive green growth; Directional distance function; Pollution reduction; Luenberger index; Convergence.

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1. Introduction China has accomplished remarkable achievements after the reform and adoption of the opening-up policy, but the driving force of its economic growth has long been factor- and resource-driven. With improvements in China‟s economy, the rapid population increase, and the acceleration of industrialization, the “black economy

and

economic

crises

involving

resource

shortages,

where

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environmental

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growth” of excessive resource exploitation and economic interests has led to

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environmental damage and damage to public health are often ignored (Wang and Shao,

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2019; Wang et al., 2019). The concept of inclusive green growth was first proposed at

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the 2012 Rio+20 Summit2. The 2015 UN Sustainable Development Goals agenda further clarified inclusive green growth, and provided ideas for China‟s economic

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transformation. China is entering a period of economic growth and structural

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transformation, effectively coordinating the relationship between resources, the environment, and economic issues, and embarking on a path of sustainable development. This precipitates a move away from resource-driven gray and black economic growth to clean, low-carbon, low-energy, and inclusive green growth; realizing changes in China‟s economic structure, growth momentum, and growth color in the context of the new economic normal. Addressing a series of other issues will determine the future of China‟s economy and profound changes in the world economic situation.

2

The Rio 2012, Rio+20, also known as United Nations Conference on Sustainable Development (UNCSD).

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Although China has been late to address economic development and resource and environmental issues, continuous efforts are now being made in this field, especially via the central government‟s exploration of the growth of the green economy (Pan et al., 2019; Yi and Liu 2015). In 2003, the Chinese government proposed the “Scientific outlook on development,” which is a formal exploration of

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the concept of green development 3 . In 2005, China proposed to “build a

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resource-conserving and environment-friendly society” to integrate resources and

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environment into its economic development strategy. In 2007, the goal of “ecological

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civilization construction” was proposed. In 2012, the plan for ecological civilization

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was incorporated into the overall “five-in-one” plan, which proposed that ecological civilization be integrated into economic, social, political, and cultural fields4. China's

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12th five-year plan identifies “inclusive green growth” as the main goal to achieve

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sustainable development5. The report of the 19th National Congress of the Communist Party of China indicated that China‟s economy has shifted from a stage of rapid growth to a stage of high-quality development. This suggests that the focus of China‟s economic development in the future should not only be to improve total factor productivity, but also, under the guidance of the five development concepts, improve the efficiency of green growth, promote the green economy, and establish and improve the economic system of green and low-carbon circular development in order

3

The report of the 17th National Congress of the Communist Party of China.

4

The report of the 19th National Congress of the Communist Party of China

5

Outline of China's 12th Five-Year Development Plan (2011-2015).

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to achieve economic and social inclusive green growth. These green development concepts and strategies are in line with each other. The core goal is to achieve green economic transformation and promote inclusive green growth. However, there is no clear definition of inclusive green growth, or a method to evaluate its core content. To solve this problem, we need to address the following

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questions. First, what is inclusive green growth and how do you evaluate the current

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mode of economic growth? Second, what are the roles of resources and environmental

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constraints in inclusive green growth? Third, what form of inclusive green growth

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should China adopt, and what consequences will this model have on China and the

embraces green growth.

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world economy? The purpose of addressing these issues is to reflect on the way China

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The contribution of this study is threefold. First, we propose a directional

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distance function (DDF) model that employs an output-oriented-slacks-based measure (SBM) model to select the directional vector. This approach avoids overestimation of efficiency and calculates the projection of each indicator at different output boundaries. This enables us to identify areas in which the level of inclusive green growth can be effectively enhanced. Similarly, the evaluation of the inclusive green growth level based on this method can also be used to explore the characteristics of China‟s potential green economy, such as the efficiency level, regional differences, and convergence path. Second，our model is integrated with the Luenberger indicator (LI) proposed by

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Chambers (2002) and Boussemart et al.(2018) to capture the dynamic change of the inclusive green growth level. Third, this paper explores China‟s inclusive green growth with respect to these issues, clarifies theories related to inclusive growth, and considers China‟s practical situation to propose an inclusive green growth model that is adapted to China‟s

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national conditions. The impact of differences within the data set is reduced because

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China‟s prefecture-level city data were used, resulting in more meaningful

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conclusions.

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The rest of the paper is structured as follows. Section 2 defines the idea of

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inclusive green growth in order to understand the depth of inclusive green growth. Section 3 describes a new directional-distance function to construct a basic model for

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measuring China‟s economic inclusive green growth. Section 4 provides an analysis

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of green economy growth level measurements in China, the driving factors behind China‟s green economy growth, and differences in the regional green growth levels. Section 5 presents our conclusions and discusses options for the further development of inclusive green growth in China.

2. Definition of inclusive green growth It is imperative to address the ecological and environmental problems faced by society. At the social level, the ultimate goals of ecological and environmental protection focus on the survival and development of human beings. The normative ideal behind preservation is concerned with the survival of the natural environment,

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especially with regard to waste of resources, environmental pollution, and loss of biodiversity. The United Nations World Commission on Environment and Development proposed the concept of sustainable development, where the goal is to “meet the needs of the present without compromising the ability of future generations to meet their own needs” (WCED, 1987). This concept of sustainable development,

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however, lacks operability because of the unknown nature of the relationship between

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economic and ecological environments that are in the course of development. In 2005,

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the United Nations Economic and Social Commission for Asia and the Pacific

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presented the concept of “green development,” which provides a more flexible and

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operational economic environment for sustainable development (OECD，2012). Unlike economic growth theory, which is dominated by GDP or social welfare, green

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growth is more beneficial to the economy, environment, inclusiveness, equilibrium,

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and sustainability. Around the world, green growth is the most widely accepted solution to stop the degradation of the natural environment (Simonis, 2012; Sohag et al., 2019; Wu et al.,2019).

Table 1 lists the main definitions of the concept of the green economy. The ultimate goal of a green economy is to achieve the development of human society. Economic and environmental relationships are the core of the green economy, which is dedicated to solving environmental degradation and social injustice issues on the basis of the existing economic system. There is a large overlap in the purpose, meaning, and method behind these concepts, but these are new ideas in the

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governance of the global economic environment, which have made positive contributions to the governance of the global green economy (Sterner and Damon, 2011; Guo et al. ,2018).

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Table 1. Definitions of green growth

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The global financial crisis seriously affected the economies of the world, and

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green growth has become a key economic strategy in various countries. However,

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developed countries and developing countries are subject to completely different

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problems in terms of the development of a green economy. Developed countries can focus on sustainability-based green growth, whereas in developing countries, poverty

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and inequality need to be addressed in the growth process, while avoiding the

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unnecessary social and environmental burdens caused by wireless growth (Luukkanen et al., 2018). The limited environmental carrying capacity of developing countries cannot support the development model of “first growth and then governance.” This has led to a different perspective on how to address growth and environmental issues in these countries. Scholars have suggested that in the context of the global economic crisis, the lower cost of environmental policy implementation has provided the opportunity to adopt more stringent environmental policies. In the context of the financial crisis, the government‟s policy center is focused on solving the problem of employment and

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poverty, and due to the bankruptcy of a large number of industrial sectors during the economic recession, energy consumption and environmental pollution levels have been reduced and policy makers have reduced confidence in environmental governance (Rick and Withagen, 2013). Therefore, several researchers believe that protecting the environment does not necessarily have to be at the expense of growth

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(Aşıcı,2013), believing instead that the two are not separate. Economic growth can

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even bring about an improvement in the natural environment, and provide a higher

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level of sustainable development for global green growth. For example, economic

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growth can improve labor productivity through improved health, eliminate market

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failures, and improve energy and environmental efficiency through subsidies. In addition, economic growth can enable more green infrastructure construction or

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2013).

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technological innovation (Barbier, 2011; Acemoglu et al., 2012; Rick and Withagen,

However, critics have questioned the concept of green growth, suggesting that it has not significantly reduced environmental pollution (Ward et al., 2016). In recent years, scholars have proposed the concept of degrowth, as replacing green growth to solve growth and environmental problems (Weiss and Cattaneo, 2017; Sandberg et al., 2019). The degrowth theory holds that the current pattern of economic growth needs to be fundamentally changed, and in order to protect the environment, it is necessary to reduce energy consumption levels and social production levels. The purpose of social development is sustainability, and GDP is not the goal of human social

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development. Of course, a reduction in the GDP is also not a consequence of this approach (Kallis, 2017). Green growth and degrowth focus on the relationship between the environment and economy in different ways, which seem to be in opposition, resulting in extensive discussion on the issue. Both theories propose a relationship between the economy

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and the environment, but there are also theoretical limitations to either approach.

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Economic growth and environmental governance cannot effectively deal with the

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current worldwide income gap and poverty. In other words, growth is not inclusive,

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and if growth is not inclusive, then growth for any purpose is unsustainable (World

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Bank, 2012). Inclusive growth is not a new concept. It is based on the theory of green growth and degrowth, emphasizing the complete harmonization of the three systems

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of the economy, environment, and society in development. The 5th Ministerial

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Conference on Environment and Development in Asia and the Pacific suggested that green growth is meant to reduce the carbon intensity of the economic system, environmental pollution, and waste of resources, and that green growth should include socially inclusive growth. Inclusive growth has played a positive role in evoking cultural values, in which citizens live in harmony with nature (Vazquez-Brust et al., 2014). The definition of inclusive green growth depends on a reasonable target determination. Scientific evaluation methods are applied, combined with the concepts of “innovation, coordination, green development and sharing” proposed by the

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Chinese government. This paper argues that an inclusive green economy is an economic development model that is determined by the three dimensions of society, economy, and environment. From a social perspective, inclusive green economic growth means improving human welfare, reducing social inequalities, and enabling the distribution of necessary items such as labor, life, and energy. From an economic

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perspective, it means that the economy is not simply defined by GDP growth, but is

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instead a green economy with continuous technological innovation, continuous

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improvement of the environment, and declining economic inequality. From an

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environmental perspective, inclusive green economic growth means “sustainable

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development” in parallel with resource conservation and environmental protection, under the conditions of ecological balance (Albagoury, 2016). China's inclusive green

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growth (IGG) can be expressed as follows:

(1)

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IGG = G(Eco，Sco，Ene，Env)

where Eco refers to economic growth, or GDP growth. China is a developing country, and various problems must be solved to enable economic development; hence, GDP growth it still an important factor. Sco refers to social equity and poverty, and capital and labor issues are key in solving these. The introduction of foreign capital after China‟s reform and the introduction of the opening-up policy is the main driving force for China‟s economic development. The lack of capital in the central and western regions and difficulties related to full employment are the main reasons for economic underdevelopment and poverty. Therefore, we consider these two factors as the main

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societal problems . Ene and Env represent energy conservation and emission reduction, respectively. Coal is the main energy source in China, and coal consumption is the main cause of environmental pollution. Assuming that the four conditions have a law of diminishing marginal returns, the function G(∙) represents the current level of inclusive green growth.

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3. Theory and calculations

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The current measure of green growth is based on statistical indicators, the most

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widely used of which is the inequality-adjusted human development index (HDI)

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(Maji, 2019; UPDP, 2019). But the HDI focuses on the degree of inequality in

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development and does not include green development. Kumar (2017) argues that in the wider ambit of sustainable development, the GDP and Human Development Index

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fails to account for sources of wealth such as nature and human progress，which is

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why the Inclusive Wealth Index (IWI) has been proposed as a more comprehensive index of development. Luukkanen et al. (2015) proposed the Sustainability Window analysis (SuWi) as an important framework for analyzing economic sustainability (Christen and Schmidt, 2012; Baumgartner and Korhonen, 2010). At the same time, this method was also used to analyze inclusive green growth (Luukkanen et al., 2019). The Data Envelopment Analysis (DEA) approach is underutilized for inclusive green growth research (Zhang et al., 2011). In fact, DEA can maximize economic output to minimize undesired output, laying the groundwork for inclusive green growth measurement (Färe et al., 1989; Chen and Geolley, 2014). In particular, the

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Directional Distance Function (DDF) and the Malmquist – Luenberger (ML) productivity index are approaches often used in making DEA an important method for green growth assessment (Chung, 1997; Li and lin, 2015; Song et al., 2017; Song et al., 2019). In the research by Zhao and Yang（2017), metafrontier-data envelopment analysis is used to evaluate the green growth development and to assess gaps in their

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efficiency in China. Song et al.,(2020) using DEA and the global Malmquist index

we have applied the DEA method to measure inclusive green

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this previous research,

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discuss the mechanism of sharing green growth across regions in China. Based on all

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growth.

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The basis of DEA is production theory, in which decision-making units (DMUs) are compared with potential common technologies to measure the relative efficiency.

𝒔

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In DEA models, the set of production possibilities 𝑆 includes input vector 𝐱 ∈ 𝑹𝑚 +, 𝒔

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output vector 𝐲 𝒈 ∈ 𝑹+𝟏 ，and undesirable outputs 𝐲 𝒃 ∈ 𝑹+𝟐 , which are expressed by the following equation:

𝑆 = {(𝐱, 𝐲 𝒈 , 𝐲 𝒃 ): 𝐱 can produce(𝐲 𝒈 , 𝐲 𝒃 )}

(2)

Färe et al. (1989, 2007) studied DEA models with undesired outputs, and argued that the efficiency of DMUs can be evaluated by setting the directional-distance function (DDF) so that the production set distinguishes between strongly disposable and weakly disposable. The DDF is an inefficiency index that measures the feasible contraction in inputs and outputs. However, the traditional DDF is prone to overestimation of the production efficiency due to radiality and orientation. Moreover,

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the efficiency evaluation result obtained based on this method does not enable the proportional adjustment of the production and input efficiency. Chung et al. (1997) combined the DEA with the DDF to form a generalized radial DEA model. The DDF analysis result (β) is independent of the input and output units. In the same set of data, as long as the direction vector remains unchanged, the input and output measurement

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units also remain unchanged. Chung et al. (1997) accounted for undesirable outputs

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with strong disposability as follows:

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⃗ (X，𝑌𝑔 ，𝑌 𝑏 ) = 𝑚𝑎𝑥𝛽 𝐷

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s. t. Xλ ≤ x0 (1 − 𝛽) g

(3)

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𝑌𝑔 𝜆 ≥ y0 (1 + 𝛽)

𝑌 𝑏 𝜆 ≥ y0b (1 − 𝛽)

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eλ ≥ 0, β free

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One of the main functions of the DDF is to be able to treat good and bad outputs differently when the indicator contains undesirable outputs. The weak disposition constraint for undesired outputs in the DDF model, which is widely recognized and applied, is a plausible approach. Despite an error in the production set of the strong disposable model of the undesired output, the correct result can be obtained. Hence, there are no weak disposition constraints on undesired outputs when the DDF processes undesired outputs. Tone (2001) proposed a non-radial, non-directed slacks-based measurement (SBM) model. The SBM is an extension of traditional DEA models that incorporates

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input excesses and output shortfalls into models that account for both of these inefficiencies simultaneously. The objective of the SBM model is to maximize the efficiency and minimize the inefficiency of inputs and outputs. From the perspective of the distance function, the projected point of the DMUs is the farthest point from the production frontier, which is contrary to the shortest path to the production frontier. 𝑠𝑖− 1 1 − 𝑚 ∑𝑚 𝑖=1 𝑥

𝑖0

+

2 ∑𝑠𝑟=1

𝑠𝑟𝑏 𝑔𝑏 ) 𝑦𝑟0

+

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1+

𝑔 1 𝑠1 𝑠𝑟 𝑔 𝑠1+ 𝑠2 (∑𝑟=1 𝑦𝑟0

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𝜌∗ = min*

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s. t. 𝑥0 = Xλ + s 𝑥 𝑔

𝑦0 = 𝑌𝑔 𝜆 − 𝑠 𝑔

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(4)

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𝑦0𝑏 = 𝑌 𝑏 𝜆 + 𝑠 𝑏 𝑠 − ≥ 0, 𝑠 𝑔 ≥ 0, 𝑠 𝑏 ≥ 0, 𝑒𝜆 ≥ 0 𝒔

𝒔

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𝒈 𝟏 𝒃 𝟐 where the slacks vectors 𝒔− ∈ ℝ𝒎 + , 𝒔 ∈ ℝ+ , and 𝒔 ∈ ℝ+ indicate input

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redundancy, that the output is insufficient, and that the undesired output is excessive and 𝜌∗ ranges from 0 to 1. If 𝜌∗ = 1, the full efficiency has been achieved, and the value of the slack vector is 0.

Fukuyama and Weber (2009) combined the SBM and DDF and proposed an SBM-DDF based on the slack measure, which effectively solves the efficiency overestimation problem and that of input and output-ratio adjustment. Based on this approach, Song et al. (2018) and Song and Wang (2018) proposed the combination of the SBM and DDF models to obtain a novel model named the DDF-SBM model. In this model, first, an SBM model with undesirable output is created and then

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used for calculating the direction in the DDF model as follows: ⃗ (X，𝑌𝑔 ，𝑌 𝑏 ) = 𝛽 ∗ = 𝑚𝑎𝑥𝛽 𝐷 s. t. Xλ ≤ x0 − β𝑔0𝑥 𝑔

𝑔

𝑌𝑔 𝜆 ≥ 𝑦0 + βg 0

(5)

𝑌 𝑏 𝜆 = y0b − βg 𝑏0 𝒈

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The direction vector to be determined is 𝒈𝟎 = (𝒈𝒙𝟎, 𝐠 𝟎 , 𝐠 𝒃𝟎 )，because 𝑔0 is

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unknown. In order to determine this vector, we define the reference targets on the

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frontier for each DMU as:

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x0∗ = x0 − β𝑔0𝑥 − 𝑆 − 𝑔∗

𝑔

𝑔

y0 = 𝑦0 + βg 0 + 𝑆 𝑔

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(6)

y0𝑏∗ = y0b − βg 𝑏0 − 𝑆 𝑏

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Färe et al. (2013) and Song et al. (2018) suggested that a unique solution can be

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obtained using the following equation: 𝑔

𝑔0𝑥 g 𝑏0 g 0 + + = 𝑚′ + 𝑠1′ + 𝑠2′ 𝑋 𝑌𝑏 𝑌 𝑔

(7)

Thus, the efficiency measure 𝛽 ′ and direction 𝑔0′ can be obtained from the slacks. Using these targets, a measure of inefficiency can be derived for each variable, as the relative slack, as follows: Input inefficiency: 𝑋0 − 𝑋0∗ β𝑋0 + 𝑆 𝑥 𝑆𝑥 = = 𝛽0∗ + 𝑋0 𝑋0 𝑋0

(8)

Good outputs: 𝑔∗

𝑔

𝑦0 − 𝑦0 𝑔

𝑦0

𝑔

=

β𝑦0 + 𝑆 𝑔 𝑔

𝑦0

=

𝛽0∗

+

𝑆𝑔 𝑔

𝑦0

(9)

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Bad outputs: 𝑦0𝑏∗ − 𝑦0𝑏 β𝑦0𝑏 + 𝑆 𝑏 𝑆𝑏 ∗ = = 𝛽 + 0 𝑦0𝑏 𝑌0𝑏 𝑦0𝑏

(10)

Based on DDF-SBM, we use the Luenberger indicator (LI) to analyze the rate of inclusive green growth. The LI was introduced by Chambers (2002) after the work of Luenberger (1992), and defined when time t is chosen as the year the technology was

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introduced as follows. When

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𝑔 𝑔 𝑔 𝑔 𝑏 𝑏 LI𝑡 (𝑋𝑡 , 𝑋𝑡+1 , 𝑌𝑡 , 𝑌𝑡+1 , 𝑌𝑡𝑏 , 𝑌𝑡+1 ) = ⃗⃗⃗⃗ 𝐷𝑡 (𝑋𝑡 , 𝑌𝑡 , 𝑌𝑡𝑏 ) − ⃗⃗⃗⃗ 𝐷𝑡 ( 𝑋𝑡+1 , 𝑌𝑡+1 , 𝑌𝑡+1 )

(11)

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𝑔 𝑔 𝑔 𝑔 𝑏 𝑏 If ⃗⃗⃗⃗ 𝐷 𝑡 ( 𝑋𝑡+1 , 𝑌𝑡+1 , 𝑌𝑡+1 ) < ⃗⃗⃗⃗ 𝐷 𝑡 (𝑋𝑡 , 𝑌𝑡 , 𝑌𝑡𝑏 ), LI𝑡 (𝑋𝑡 , 𝑋𝑡+1 , 𝑌𝑡 , 𝑌𝑡+1 , 𝑌𝑡𝑏 , 𝑌𝑡+1 ) > 0, there

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is inclusive green growth; otherwise, there is no inclusive green growth. This is also

𝑔

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defined when time t+1 is chosen as the base year for the technology as follows: 𝑔

𝑔

𝑔

(12)

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𝑏 𝑏 𝑡+1 (𝑋 ，𝑌 ，𝑌 𝑏 ) − 𝐷 𝑡+1 ( 𝑋 ⃗⃗⃗⃗⃗⃗⃗⃗⃗ ⃗⃗⃗⃗⃗⃗⃗⃗⃗ LI𝑡+1 (𝑋𝑡 , 𝑋𝑡+1 , 𝑌𝑡 , 𝑌𝑡+1 , 𝑌𝑡𝑏 , 𝑌𝑡+1 )=𝐷 𝑡 𝑡+1 ，𝑌𝑡+1 ，𝑌𝑡+1 ) 𝑡 𝑡

In order to eliminate the impact of the technology cycle on the growth indicators

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of inclusive green economy, an arithmetic mean of the Luenberger productivity indicator based on t and t+1 is used to average the effect of selecting an arbitrary year. Hence, LI is set between any two periods: LI =

1 𝑔 𝑔 𝑔 𝑔 𝑏 [LI (𝑋 , 𝑋 , 𝑌 , 𝑌 , 𝑌 𝑏 , 𝑌 𝑏 ) − LI𝑡+1 (𝑋𝑡 , 𝑋𝑡+1 , 𝑌𝑡 , 𝑌𝑡+1 , 𝑌𝑡𝑏 , 𝑌𝑡+1 )] 2 𝑡 𝑡 𝑡+1 𝑡 𝑡+1 𝑡 𝑡+1

1 𝑡+1 𝑔 𝑔 𝑔 𝑔 𝑏 𝑡+1 ⃗⃗⃗⃗⃗⃗⃗ = [⃗⃗⃗⃗⃗⃗⃗ 𝐷 (𝑋𝑡 , 𝑌𝑡 , 𝑌𝑏𝑡 ) − 𝐷 ( 𝑋𝑡+1 , 𝑌𝑡+1 , 𝑌𝑏𝑡+1 ) + ⃗⃗⃗⃗ 𝐷𝑡 (𝑋𝑡 , 𝑌𝑡 , 𝑌𝑡𝑏 ) − ⃗⃗⃗⃗ 𝐷𝑡 (𝑋𝑡+1 , 𝑌𝑡+1 , 𝑌𝑡+1 )] (13) 2 Chambers et al. (1996) showed that LI can be decomposed as follows (Boussemart et al., 2018): LI =

1 𝑔 𝑔 𝑔 𝑔 𝑏 𝑏 [LI𝑡+1 (𝑋𝑡 , 𝑋𝑡+1 , 𝑌𝑡 , 𝑌𝑡+1 , 𝑌𝑡𝑏 , 𝑌𝑡+1 ) − LI𝑡 (𝑋𝑡 , 𝑋𝑡+1 , 𝑌𝑡 , 𝑌𝑡+1 , 𝑌𝑡𝑏 , 𝑌𝑡+1 )] 2 𝑔 𝑏 ⃗⃗⃗⃗𝑡 (𝑋𝑡 , 𝑌 𝑔 , 𝑌𝑡𝑏 ) − ⃗⃗⃗⃗⃗⃗⃗⃗⃗ = [𝐷 𝐷𝑡+1 (𝑋𝑡+1 , 𝑌𝑡+1 , 𝑌𝑡+1 )] 𝑡

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1 𝑡+1 𝑔 𝑔 𝑔 𝑔 = [⃗⃗⃗⃗⃗⃗⃗ 𝐷 (𝑋𝑡+1 , 𝑌𝑡+1 , 𝑌𝑏𝑡+1 ) − ⃗⃗⃗ 𝐷𝑡 (𝑋𝑡+1 , 𝑌𝑡+1 , 𝑌𝑏𝑡+1 ) + ⃗⃗⃗⃗⃗⃗⃗⃗⃗ 𝐷𝑡+1 (𝑋𝑡 , 𝑌𝑡 , 𝑌𝑡𝑏 ) − ⃗⃗⃗⃗ 𝐷𝑡 (𝑋𝑡 , 𝑌𝑡 , 𝑌𝑡𝑏 )] (14) 2 The expressions in the first and second sets of brackets represent technical efficiency change (EC) and technical change (TC), respectively. Based on the notion of input-neutral TC and output-neutral TC, Briec et al. (2011) considered the possibility of both input- and output-biased TCs. We define output-biased technical

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change (OBTC) as follows:

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1 ⃗⃗⃗⃗𝑡 𝑔 𝑔 𝑔 𝑔 𝑏 𝑂𝐵𝑇𝐶 = [(𝐷 (𝑋𝑡+1 , 𝑌𝑡+1 , 𝑌𝑡+1 ) − ⃗⃗⃗⃗⃗⃗⃗ 𝐷𝑡+1 (𝑋𝑡+1 , 𝑌𝑡+1 , 𝑌𝑏𝑡+1 )) + (⃗⃗⃗⃗⃗⃗⃗ 𝐷𝑡+1 (𝑋𝑡 , 𝑌𝑡+1 , 𝑌𝑏𝑡+1 ) − ⃗⃗⃗ 𝐷𝑡 (𝑋𝑡+1 , 𝑌𝑡 , 𝑌𝑏𝑡 ))] (15 2 Symmetrically, input-biased technical change (IBTC) is defined as follows:

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1 𝑔 𝑔 𝑔 𝑏 ⃗⃗⃗⃗𝑡 (𝑋𝑡+1 , 𝑌 𝑔 , 𝑌𝑡+1 𝐼𝐵𝑇𝐶 = [((⃗⃗⃗⃗⃗⃗⃗ 𝐷𝑡+1 (𝑋𝑡 , 𝑌𝑡 , 𝑌𝑏𝑡 ) − ⃗⃗⃗ 𝐷𝑡 (𝑋𝑡 , 𝑌𝑡 , 𝑌𝑏𝑡 )) + (𝐷 ) − ⃗⃗⃗⃗⃗⃗⃗ 𝐷𝑡+1 (𝑋𝑡+1 , 𝑌𝑡+1 , 𝑌𝑏𝑡+1 ))](16) 𝑡 2

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Hence, it is possible to obtain the magnitude of technical change (MATC): 𝑇𝐶 = 𝐼𝐵𝑇𝐶 + 𝑂𝐵𝑇𝐶 + 𝑀𝐴𝑇𝐶

(17)

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for period t as follows:

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where MATC measures the magnitude of technical change in direction g using data

𝑔 𝑡+1 (𝑋 ，𝑌 𝑔 ，𝑌 𝑏 ) ⃗⃗⃗⃗⃗⃗⃗⃗⃗ 𝑀𝐴𝑇𝐶 = ⃗⃗⃗⃗ 𝐷 𝑡 (𝑋𝑡 ，𝑌𝑡 ，𝑌𝑡𝑏 ) − 𝐷 𝑡 𝑡 𝑡

(18)

4. Results 4.1 Basic characteristics of inclusive green growth The data for China‟s prefecture-level cities only contain relatively complete statistics after 2003. In 2016, the statistical caliber of China‟s sulfur dioxide (SO2) was changed. Hence, this indicator is no longer comparable for the periods before and after 2016. In addition, some parts of the dataset where data loss was significant were

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removed, and data were selected from 285 prefecture-level cities from 2003 to 2015. The data used in this study were obtained from the EPS database6, the China Regional Statistical Yearbook, and the China City Statistical Yearbook. According to the DEA model constructed in the third part, the indicators used in the measurement of the inclusive green growth level in China include input indicators,

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expected output indicators and undesired output indicators. The essence of inclusive

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green growth is to seek a balance between economic growth and environmental

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protection. Therefore, we use the factor input of the national economy as an input

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indicator, with economic output as an output indicator and the environment as an

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undesired output. Factor inputs include labor, capital and energy, economic output is represented by GDP, and environmental undesired outputs include water pollution,

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sulfur dioxide, and industrial soot emissions. The relevant variables and data for

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efficiency evaluation are shown in Table 2.

Table 2. Input-output indicators, data, and description of inclusive green growth

The DEA model of this paper can directly measure the economic inefficiency value, and calculate the economic efficiency of the prefecture-level city level according to the inefficiency value. We define it as the green economy efficiency. Further, the Luenberger Index defines the change in the green economy from t to t+1

6

http://olap.epsnet.com.cn/

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years, which is the green growth capacity of the economy, which we define as the level of inclusive green growth. That is to say, inclusive green growth is a measure of the dynamic level of green economy efficiency at the regional level in China. In order to study the overall characteristics of the level of China‟s inclusive green economy, inclusive green growth levels are mapped to corresponding regions in China,

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as shown in Fig. 1. From the perspective of inclusive green growth, except for parts of

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the northeast, there are no clear spatial agglomeration effects in most regions. The

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average LI value for inclusive green growth in most of the prefecture-level cities is

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greater than 0, which means that China's emphasis on environmental governance and

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rapid economic growth have led to a significant increase in inclusive green growth. However, there is still no significant increase in inclusive green growth in certain

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regions, and the pressure on China‟s environmental governance is still relatively high.

Fig. 1. Inclusive green growth mean in regions in China

The target values for energy efficiency and undesired output required to achieve optimal efficiency were calculated, and the energy saved and emission reduction for energy input and pollutant discharge, respectively, are determined. As shown in Table 3, the energy reduction ratio decreases and then increases. From 2003 to 2005, the energy input is reduced by more than 40 %; from 2006 to 2013, it is reduced by less than 40 %, and subject to fluctuations; and in 2014 and 2015, the energy input is again

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reduced by more than 40 %. In terms of pollutant emissions, the proportion of wastewater discharge is reduced in an irregular manner, and the proportion of sulfur dioxide and soot emissions is reduced. In recent years, the Chinese government has adopted a series of energy-saving and emission-reduction measures, focusing on SO2 emissions and PM2.5 and PM10. Therefore, SO2 and soot pollutants are reduced to a

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higher degree in recent years, and the reduction ratio of wastewater discharge is

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significantly lower than that of the other two pollutants. This shows that the most

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critical factor in China's environmental governance is policy orientation, and

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government policy change is key to achieving green and inclusive growth (Song et al.,

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2018).

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Table 3. Reduction in energy input and pollutant emissions (%)

Table 4 shows the annual average of China‟s inclusive green growth indicators and their decomposition factors. The LI value from 2004 to 2006 is negative, indicating that China's inclusive green growth level was low at this stage. Except in 2011, the inclusive green growth indicators (LI values) were positive from 2007 onwards, indicating an increase in inclusive green growth. Before 2006, China‟s economy was in a period of rapid development. Hence, the GDP and energy consumption increased rapidly, and insufficient attention was paid to inclusive green growth, resulting in a sharp deterioration of the ecological environment. Therefore, in

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this period, it was in the stage of “black growth” or “gray growth.” However, after the introduction and implementation of the scientific development concept after 2003, China began to address resource conservation and environmental protection while promoting GDP growth. Although the degree of economic greening has improved over time, the economy is still in a state of “gray growth” due to the limitations of the

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industrial structure and technical resources.

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Table 4. Average level of China’s green economy growth and its decomposition

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The decomposition of LI in Table 4 indicates that EC is the main driver of inclusive green growth. In most years, the EC index is positive and follows a rising

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trend, but the TC contributes less to inclusive green growth and exhibits the opposite

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trend. TC represents the difference between the optimal inclusive green growth and existing inclusive green growth. A further decomposition of the TC shows that this difference is mainly due to the MATC. The OBTC and IBTC values are relatively close, indicating that input-biased technological change and output-biased technological change do not differ significantly, and that both have a positive effect on inclusive green growth. This implies that China‟s policy of reducing pollution emissions and increasing energy saving as a part of inclusive economic green growth has somewhat inhibited further deterioration of the environment. The sum of the OBTC and IBTC is less than the MATC value, indicating that a reduction in the

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degree of technological change has impaired China‟s inclusive green growth. The initial impetus for China‟s technological progress comes from the technology spillover effect of foreign direct investment. However, as China‟s economic development and labor costs increase, this technological power is reduced. At the same time, the lack of independent innovation capability also limits the level of

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inclusive green growth. Therefore, China‟s promotion of inclusive green growth is

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increase the speed of technological innovation.

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mainly to enhance the TC, especially from the perspective of the MATC, in order to

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4.2 Convergence analysis of inclusive green growth

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In order to verify the sustainability of China‟s inclusive green growth and the convergence of growth paths, σ convergence (the difference between regional

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inclusive green economic growth level and the overall average level; the dynamic

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imbalance of inclusive green economic growth over time) and β convergence are used to verify regional differences in the convergence of inclusive green growth. The σ convergence value is defined by the following expression: 𝑛

𝑛

1 1 𝜎𝑡 = √ ∑(𝐿𝐼𝑖𝑡 − ∑ 𝐿𝐼𝑖𝑡 )2 𝑛 𝑛 𝑖=1

𝑖=1

where 𝑖 and 𝑡 represent regional and time variables, respectively, 𝑛 represents the prefecture-level city total, and 𝐿𝐼𝑖𝑡 is the logarithm of inclusive green growth. If 𝜎𝑡+1 < 𝜎𝑡 , then China‟s inclusive green growth level is considered to exhibit σ convergence.

(19)

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According to convergence theory, regions with low levels of inclusive green growth may have higher growth rates in the development process due to advantages related to late development. In order to test if this is true in China's case, we used β convergence to test the steady-state level of a prefecture-level city. β convergence can be divided into absolute β convergence and conditional β convergence,

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depending on whether other factors are controlled. The basic model is as follows: (𝐿𝐼𝑖𝑡 − 𝐿𝐼𝑖0 ) = α + β𝐿𝐼𝑖0 + 𝜀𝑖𝑡 t (𝐿𝐼𝑖𝑡 − 𝐿𝐼𝑖0 ) = α + β𝐿𝐼𝑖0 + 𝜆𝑖 𝑋𝑖 + 𝜀𝑖𝑡 t

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(20)

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(21)

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where 𝐿𝐼𝑖𝑡 is the logarithm of the inclusive green growth level and 𝐿𝐼𝑖0 is the

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logarithm of the initial value of the inclusive green growth. α is a constant term, β and 𝜆𝑖 are parameters, 𝑋𝑖 is a control variable, indicating that the external factors

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influence convergence, and 𝜀𝑖𝑡 is random error. If β < 0 and the significance test is

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passed, the inclusive green growth converges; otherwise, there is a divergence trend. In this study, only indicators that may affect inclusive green growth are selected as the control variables 𝑋𝑖 , including (1) density, (2) regional per capita income level, (3) industrial structure, and (4) the investment openness indicator.

First, density

(dens) is expressed as the regional population density difference, which varies significantly between eastern, central, and western China, and generally the energy consumption and pollution levels are relatively high in areas with a high population density. Next, the regional per capita income level (pgdp) controls the impact of regional economic level differences on the convergence of inclusive green growth.

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Furthermore, an upgrade in the industrial structure (is) in this study is thought to indicate that the proportions of primary and tertiary industry are gradually decreasing and increasing, respectively. The overall law of industrial development is an aim to transform primary and secondary industries into tertiary industries. The industrial structure upgrading index used in this study is expressed as follows: is = ∑3𝑖=1 𝑝𝑖 ∙ 𝑖,

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𝑖 ≤ 𝑖𝑠 ≤ 3, where 𝑝𝑖 is the proportion of tertiary industry among the three industries.

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Finally, the investment openness indicator (fdi) is based on the proportion of foreign

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direct investment in the GDP, where the US dollar is converted into ChiNaYuan (CNY)

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according to the average foreign exchange rate of the year.

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Fig. 2 shows the trends in the σ convergence coefficient at the national level, and for the eastern, central, and western regions from 2004 to 2015. Based on an

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overall perspective at the national level, the characteristics of the stages of China‟s

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inclusive green growth are clear. Relatively unstable fluctuations are observed before 2010, and there is no convergence trend. However, an upward trend is observed from 2010 to 2013, indicating that there is no σ convergence at this stage, and differences in the inclusive green growth levels between regions begin to rapidly increase. After 2013, with the emphasis on inclusive green growth, environmental governance actions were gradually introduced, as a result of which a convergence in regional economies is observed, which is higher than that before 2010. In terms of the sub-regions, there is a difference in convergence between the eastern, central, and western regions from 2004 to 2006, and the σ value in the

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western region is higher than that in the central region. There is no convergence between the three regions from 2006 to 2010. The regional σ convergence begins to differ from 2010 to 2013, and the σ values in the central and western regions increase, indicating an increase in internal divergence.

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Fig. 2. σ convergence coefficients for China, and its eastern, central, and

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western regions, from 2004 to 2015

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The σ convergence data indicates the convergence of regional differences

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during China‟s inclusive green growth from the perspective of stocks, prompting further analysis of the β convergence from the perspective of long-term trends. In

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order to overcome the endogeneity problem and improve model estimation accuracy,

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the system generalized method of moments (SGMM) method was used to determine the β convergence model. The Im-Pesaran-Shin unit-root (IPS) test, Levin-Lin-Chu unit-root (LLC) test, and Breitung test are used to test the plateau residual stability. The results show that the test was rejected at the 1 % significance level for both the national sample and the eastern, central, and western samples. The Pearson correlation coefficient and a variance inflation factor (VIF) test on the explanatory variables do not indicate a multicollinearity problem. The Arrellano-Bond AR(1) and AR(2) tests of residual sequence correlation show that there is a first-order sequence correlation of the perturbation term based on the SGMM model, but there is no second-order

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sequence correlation. The Sargan test results show that the null hypothesis that “all instrumental variables are valid” cannot be rejected, which confirms the rationality of the instrumental variables selected in this study. Therefore, the SGMM results are valid and credible. The estimated results are shown in Table 5.

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Table 5. β convergence of China’s national and sub-regional inclusive green

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growth

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According to the results in Table 5, at the national level, the estimated β

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coefficient is less than zero and the significance test is passed at 1% significance level, indicating an obvious β convergence and that areas with low inclusive green growth

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levels catch up with areas with high inclusive green growth levels. At the sub-regional

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level, the estimated values in the eastern, central, and western regions are all less than 1, which implies significant β-convergence, indicating a catch-up trend of inclusive green growth within the regions. Based on the absolute value of the β coefficient, the convergence within each region is greater than the national convergence. This is because the analysis of the national convergence ignores the intra-group differences between eastern, central, and western China. The absolute β convergence and the conditional β convergence grew faster in the eastern and western regions, and the internal differences in the eastern region are relatively small. This is because the catch-up between regions is based on a solid economic foundation. The economy in

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the western region is weak, but the government has always supported development there. In particular, the ecological environmental conditions in the western region are better than those in the eastern and central regions. Therefore, the western region is subject to significant latecomer advantages with respect to inclusive green growth. The internal differences in the central region are much larger than in the east and west;

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hence, internal convergence exists, but the degree of convergence is lower than in the

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east and west.

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5. Conclusion and further discussion

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5.1 Main conclusions

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This paper combined inclusive green growth with the characteristics of China‟s economic development and proposed that China‟s inclusive green growth can be

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described by a development model that includes economic, social, energy, and

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environmental dimensions. This growth model not only considers the resource and environmental concerns of the traditional green economy growth concept, but also considers the inclusion of social issues in developing China. DDF-SBM and LI were combined to construct an evaluation model of inclusive green economic growth. The inclusive green growth levels of 285 prefecture-level cities in China were measured from 2003 to 2015, and the main conclusions are as follows: (1) Our initial approach involved scrutinizing the critical characteristics of green economic growth. In general, there were significant regional differences in the level of inclusive green economy growth, and the spatial and temporal distributions of the

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green economy efficiency and green economy growth were inconsistent. With regard to input and output indicators, we found that energy and wastewater discharges did not decrease, indicating that the levels of energy consumption and wastewater discharge still impair China‟s inclusive green growth. Relatively speaking, the Chinese government has achieved good results in terms of sulfur dioxide and soot

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treatment, resulting in a reduction in these two pollutants. In addition, the

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decomposition of the LI indicated that the speed of technological progress lags behind

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China‟s inclusive green economy growth.

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the speed of energy consumption, and environmental pollution is an obstacle to

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(2) Regional differences in green economic growth were observed. The σ convergence and β convergence were used to analyze the convergence of inclusive

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green growth paths, both at the national level and in the eastern, central, and western

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regions of China. The σ convergence characteristics of China‟s inclusive green growth were not clear, which indicates that from the perspective of stocks, inclusive green growth in China is in a divergent state. The β convergence characteristics were significant. The convergence in the eastern and western regions is greater than that in the central region and at the national level, indicating a convergence trend in China‟s inclusive green growth. Based on the above findings, we recommend the following policy changes: (1) To enhance the rate of technological progress. According to the decomposition of the Luenberger index, the arbitrary nature of China's technological

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progress is the main obstacle to the improvement of inclusive green growth. For this reason, the policy should emphasize research and development investment in environmental governance, and should not only consider the environment and energy biased technological progress, but also the coordination of technical progress, promoting the overall advancement of technology (Zhou and Feng, 2017). First, under

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the dual pressures of China's resource constraints and pollution reduction, China

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should strengthen management from both of energy input and pollution emission,

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promote energy conservation and pollution emission standards, and regard green

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technological progress as the fundamental driving force for inclusive green growth.

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Secondly, the efficiency of technological progress should be improved, the current problem of insufficient green technology investment should be solved, the level of

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green technology investment should be strengthened, and long-term strategies for

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green technology progress should be formulated. (2) Promote the comprehensive management of pollution. China faces serious pollution problems, but air pollution is apparent to every city dweller in China and is being addressed on a global scale (such as PM2.5). Chinese intending to solve environmental problems need to focus on various aspects of the environment, should promote the overall governance of the environment from air pollution, water pollution, solid waste and noise pollution, and must avoid bias in environmental governance. (3) Narrowing regional differences. Regional differences and local competition have led to greater pressure on China's inclusive green growth. The spread of

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pollutants to developing regions has reduced the overall level of inclusive green growth in China. Therefore, China needs to establish a sound mechanism for coordinated regional development, reduce regional disparities, and avoid a "treatment after pollution" path of development in central and western regions. 5.2 Further discussion

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The differing concepts and evaluation approaches to inclusive green growth lead

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to different estimation results, due to the different systems and indicators employed.

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The concept of economic green growth needs to be further defined and clarified due

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to the limitations of indicators and a lack of inclusive green economy growth practices

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in China. It is also important to adopt an appropriate model to measure the level of inclusive green growth in future research. Although this study is not entirely

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comprehensive with regards to the definition of inclusive green growth, this simple

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and positive approach may contribute to promoting inclusive green growth in other developing countries.

In addition, green growth and degrowth are completely different growth paradigms, and both have proved to play a reasonable role in the process of economic green growth. Although in practice, green growth prioritizes the paradigm outcome of economic growth, while degrowth gives priority to environmental protection (Vazquez-Brust et al., 2014; Sandberg et al., 2019). As inclusive green growth is still a future-driven concept, there are many uncertain factors related to it. In order to achieve social development and protect the ecological environment, inclusive green

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growth and degrowth need to be combined with the overall development goals of society in order to address the shortcomings of current theories and practices, and obtain a better balance between economic and environmental concerns.

Funding This work was supported by the Major Programs of National Fund of Philosophy

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and Social Science of China (Grant No. 18ZDA126),the National Natural Science

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Foundation of China (71873001) and the Support Plan for Innovative Talents in

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Colleges and Universities of Liaoning Province (Grant No. WR2017007).

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Author contributions

manuscript.

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Conflicts of interest

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All authors contributed to the scientific content and authorship of this

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The authors declare no conflict of interest.

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Declaration of interests

☒ The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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may be considered as potential competing interests:

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☐The authors declare the following financial interests/personal relationships which

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Fig. 1. Inclusive green growth means in regions in China

Fig. 2. σ convergence coefficients for China, and its eastern, central, and western regions, from 2004 to 2015

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Table 1 Definitions of green growth Institution

Definition “About fostering economic growth and development while ensuring that natural assets continue to provide the resources and

The Organization for environmental services on which our well-being relies. It is also Economic Co-operation about fostering investment and innovation, which will underpin and Development

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sustained growth and give rise to new economic opportunities” (OECD, 2011).

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“Improved human well-being and social equity, while significantly The United Nations

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reducing environmental risks and ecological scarcities” (UNEP, Environmental Program

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2011)

United Nations

A green economy can improve human well-being and social equity

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Conference on

while greatly reducing environmental risks and ecological scarcity Sustainable

(RIO+20, 2012).

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Development

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“Green growth is growth that is efficient in its use of natural

The World Bank

resources, clean in that it minimizes pollution and environmental

(World Bank, 2012)

impacts, and resilient in that it accounts for natural hazards” (Fay, 2012; World Bank; 2012).

“The aim is to create more value while using fewer resources, and

The European substituting them with more environmentally favorable choices Commission wherever possible” (European Commission, 2016).

Table 2 Input-output indicators, data, and description of inclusive green growth Index

Variable

Input

Labor

Data and description The total number of employees in the prefecture-level city at the end of the year, expressed

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in units of “10,000 people.” When selecting capital indicators, most scholars currently use the perpetual inventory method to calculate the capital stock. However, in the calculation process, there is a significant difference in the choice of the base period capital stock and the depreciation rate. In this study, we assume that the growth trends of fixed asset investment and fixed capital formation in a society are consistent. The DEA method measures relative efficiency and ensures that the sample data produces a relatively consistent analysis result, without large deviations. Therefore, this study uses the total investment in fixed assets of the whole society as a capital input indicator. Eliminate price factors are based on 2003. This is expressed in units of “10,000 Yuan.” There is lack of data on energy consumption in prefecture-level cities in China, but there is a strong correlation between electricity and energy consumption. The common practice is to use energy consumption data from such cities. This is expressed in units of “10,000 kWh.” The constant-price GDP was obtained with 2003 as the base year, which is expressed in units of “10,000 Yuan.” This is based on the total discharge of industrial wastewater in prefecture-level cities, expressed in units of “10,000 tons.” This is based on the total amount of sulfur dioxide emissions in the city, expressed in units of “tons.” After 2011, the combined statistics for soot and dust are used to calculate the soot emissions of a comparable caliber from 2011 to 2015, in units of “tons” based on the proportion of soot emissions from 2010 to soot (dust) emissions.

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GDP

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Good output

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Energy

Wastewater

Undesirabl e output

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Capital

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Sulfur Dioxide

Industrial Soot

Table 3 Reduction in energy input and pollutant emissions (%) Year

Energy

Wastewater

Sulfur dioxide

Industrial Soot

2003

40.47

57.55

61.73

55.30

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40.43

66.30

65.70

64.85

2005

42.62

65.36

60.33

61.22

2006

38.61

63.99

63.56

57.28

2007

37.97

63.33

60.32

63.70

2008

38.40

59.56

60.61

61.97

2009

36.96

66.65

62.59

67.93

2010

38.56

63.04

63.74

64.27

2011

37.73

59.85

50.68

57.98

2012

38.73

57.23

51.64

56.72

2013

38.49

49.29

53.06

52.20

2014

44.74

58.80

52.67

57.71

2015

51.63

61.73

56.24

58.50

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2004

Table 4

LI

EC

TC

OBTC

IBTC

MATC

2004

-0.0100

0.0085

-0.0185

0.0464

0.0468

-0.1117

2005

-0.0022

0.0238

-0.0260

0.0391

0.0518

-0.1169

2006

-0.0026

-0.0019

-0.0007

0.0382

0.0506

-0.0895

2007

0.0170

0.0045

0.0125

0.0400

0.0480

-0.0754

2008

0.0368

0.0201

0.0167

0.0513

0.0443

-0.0789

2009

0.0355

-0.0038

0.0393

0.0343

0.0579

-0.0529

2010

0.0247

0.0216

0.0031

0.0379

0.0389

-0.0737

2011

-0.0172

0.0340

0.0514

0.0305

-0.1330

2014 2015

na

2013

-0.0512

0.0213

0.0010

0.0202

0.0281

0.0513

-0.0591

0.0660

0.0670

-0.0010

0.0627

0.0591

-0.1228

0.0007

0.0290

-0.0282

0.0726

0.0693

-0.1702

0.0303

0.0749

-0.0447

0.0477

0.0497

-0.1420

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2012

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Year

re

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Average level of China’s green economy growth and its decomposition

Note: EC indicates the technical efficiency change; TC indicates the technical change; OBTC indicates the output-biased technical change; IBTC indicates the input-biased technical change; and MATC indicates the magnitude of technical change.

Table 5 β convergence of China’s national and sub-regional inclusive green growth Coefficient

National

East

Central

Western

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β

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-0.1678***

-0.1368***

-0.3218***

-0.3352***

-0.2303***

-0.3064***

-0.4142***

-0.3072***

（0.0038）

(0.0262)

(0.0003)

(0.0012)

(0.0033)

(0.0501)

(0.0016)

(0.0012)

-0.1081

-0.0108***

-0.0151

0.0493***

(0.0113)

(0.0019)

（0.0094）

(0.0009)

-0.0705***

0.0153***

-0.0923***

-0.0052***

(0.0091)

(0.0005)

(0.0136)

(0.0010)

0.0444***

-0.0252***

0.0219***

0.0848***

(0.0092)

(0.0011)

(0.0064)

(0.0005)

0.0120***

0.0019***

0.0472***

0.0069***

(0.0029)

(0.0002)

(0.0070)

(0.0001)

lndens

lnpgdp

lnis

lnfdi 0.7680***

0.0063***

-0.0445***

0.0040***

1.1015***

-0.0075***

-0.2555***

（0.0008）

（0.1137）

(0.0002)

(0.0094)

(0.0002)

(0.1165)

(0.0004)

(0.0063)

-1.72*

-1.81*

-2.03**

-2.03**

-1.26

-2.01**

-1.83*

-1.80*

[0.086]

[0.070]

[0.042]

[0.043]

[0.208]

[0.045]

[0.068]

[0.072]

1.66*

1.56

-0.17

-0.11

1.37

1.64

1.49

1.16

[0.097]

[0.118]

[0.863]

[0.911]

[0.171]

[0.101]

[0.135]

[0.246]

30.53

58.75

86.95

84.13

42.30

28.18

64.80

66.56

[0.100]

[0.485]

[0.184]

[0.155]

[1.55]

[0.660]

[0.086]

[0.569]

Hansen

5.70

3.92

4.75

6.31

14.97

9.80

12.10

1.90

Test

[0.681]

[0.916]

[0.943]

[0.852]

[0.092]

[0.458]

[0.356]

[0.999]

of

0.0038*** Intercept

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Sargan Test

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AR（2）

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AR（1）

Note: () indicates standard errors; [] indicates the P value; and ***, ** and * indicate significance at the 1 %, 5 %,

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and 10 % levels, respectively.

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Graphical abstract

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Highlights: 

An inclusive green growth model for China is evaluated



DDF-SBM is used to measure the inclusive green growth potential



LIs and their decompositions measure inclusive green growth and driving factors

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 A convergence trend in inclusive green growth is observed