The evolution and a temporal-spatial difference analysis of green development in China

Sustainable Cities and Society 41 (2018) 52–61

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The evolution and a temporal-spatial difference analysis of green development in China

T

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Caizhi Suna, , Yanli Tongb, Wei Zouc a

China Institute of Boundary and Ocean Studies, Wuhan University, No. 299 Bayi Road, Wuhan 430072, China Center for Studies of Marine Economy and Sustainable Development, Liaoning Normal University, No. 850 Huanghe Road, Dalian 116029, China c School of Foreign Languages, Liaoning Normal University, No. 850 Huanghe Road, Dalian 116029, China b

A R T I C LE I N FO

A B S T R A C T

Keywords: Green development Information entropy Evolution Temporal-spatial analysis

According to the dissipative structure theory, this study establishes an indicator system and information entropy model to analyze the evolution of green development. The integrated weighting method is used to assess the level of green development. Finally, we analyze the level of green development from temporal and spatial perspectives. The results show that the total entropy of almost all provinces and cities in China has been reduced, indicating that the degree of order of the green development system has been strengthened. The level of green development has increased from 2000 to 2014 but overall was not high. From a temporal view, the level of green development converges with a big gap. From a spatial view, there are tremendous differences among the eastern, central, and western areas. Based on these findings, we put forth reasonable suggestions for promoting the level of green development in China.

1. Introduction Development is the theme and common pursuit of human society, while the natural environment is the basic prerequisite for the survival and development of human society; understanding the challenges that exist between supporting the environment and development economics are very important (Barbier, 2014). Since the start of the 21st century, warming of the global climate has become increasingly prominent. Ecological, environmental, resource, and climate crises and other phenomena, such as the “greenhouse effect,” atmospheric ozone depletion, soil erosion, deforestation, land desertification, and water pollution, occur frequently, exacerbating shortages of global environmental resources. As observed in Brown (2004), losses caused by climate change were estimated by the United Nations to be $150 billion a year by 2010. In 2008, the world was faced with multiple crises (fuel, food, and finance). Facing such new energy and climate crises, human beings must adapt to the future “green prospect” and develop green, low-carbon technologies (Makower & Pike, 2008). In this context, the idea of a “green economy” has become more prominent internationally, particularly in developed countries (Ciocoiu, 2011). In both Europe and Asia, developed economies upgrade their technology, transform their industrial structures, and formulate regulations to control energy prices and inflation, gradually mitigate energy dependency, and lower carbon emissions; developing countries take on the challenge of designing their

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own climate change governance at a standard comparable to that of developed countries (Tsai, Lee, Yang, & Huang, 2016). Green development has a long history. Pearce, Markandya, and Barbier (1989) explained that the green economy advocated for an “affordable economy” and proposed including the costs of harmful activities and exhausted resources in the national balance sheet. Further, they stated that economic development should take the capacity of the natural ecological environment into account fully as the green economy first began to appear worldwide. With economic growth as its goal, the Organization for Economic Cooperation and Development (OECD Indicators, 2011) designed an indicator system that included environmental and resource productivity, natural asset base, quality of life, and policy response. Over time, the theory of green development has gradually matured. The goals of the green economy have evolved from achieving an ecological or economic-ecological system to achieving an economic-social-ecological system, which has laid a solid theoretical foundation for the further study of green development (Shen, Ma, Xie, & Wang, 2014). Economic activities, all of which are subject to the constraints of ecological parameters, take place within a social network (Malone et al., 2014), and an understanding of the relationship between the socio-economic and natural ecological environment has long been an important goal in the process of green development. The link between exergy (Sciubba & Wall, 2007) and entropy is a thermodynamic approach to the analysis of the unavailability of economic, productive,

Corresponding author. E-mail addresses: [email protected] (C. Sun), [email protected] (Y. Tong), [email protected] (W. Zou).

https://doi.org/10.1016/j.scs.2018.05.006 Received 26 February 2017; Received in revised form 17 February 2018; Accepted 3 May 2018 Available online 18 May 2018 2210-6707/ © 2018 Elsevier Ltd. All rights reserved.

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2. Materials and methods

or social systems (Lucia, 2016), and the unavailability percentage can be used to support sustainable development (Lucia & Grisolia, 2017). As green development is a highly intricate system composed of economic, social, and ecological environments, many factors should be considered in its evaluation. Many studies have developed complex and multi-target integrated index systems to analyze the level of green development. Nevertheless, they still remain limited. In a report on China’s green development index, a relatively complete green economy evaluation index system was set forth, and the differences in the green economy of 30 provinces and cities in China were analyzed. While the method of assigning weights has some limitations, it almost entirely uses the subjective analysis of the Delphi method for weighting and, with only one year of data, could not achieve a dynamic comparison (Scientific Development and Economic Sustainable Development Research Base of Beijing Normal University, Green Economy and Economic Sustainable Development Research Base of Southwestern University of Finance and Economics, and China Economic Monitoring and Analysis Center of National Bureau of Statistics, 2015). Guo, Mi, and Zhao (2015) used an entropy method, improved TOPSIS model, and obstacle degree model to assess the spatial differentiation of and factors influencing the green development level of Ningxia but could not analyze it in terms of the time dimension. Zhao, Lin, and Chen (2011) took California as an example and introduced foreign countries that established a measurement of the green economic development index system, but they too heavily emphasized the low-carbon economy, ignoring the economic base and the support of natural resources. According to the World Bank (2009), China has become the world’s second largest energy consumer and the largest emitter of carbon dioxide. Current studies generally agree that China’s energy consumption and carbon emissions have played a significant role in global carbon dioxide emissions (Geng & Shi, 2014). Since 2007, a series of papers has been published on the implementation of the green economy, while the concept of “greenization” as a national strategy was proposed for the first time in 2015. China’s green development faces serious problems. Resource-intensive and labor-intensive products are the major driving forces of the rapid growth of China's economy, aggravating resource scarcity and environmental degradation (Li, 2014). Furthermore, Qian and Liu (2014) showed that the pollution control coefficient in the country is negative but not significant, indicating that the government has not achieved its expected improvements despite its environmental control measures. The implementation of green development is not a short-term process. In order to get results, we must carry out long-term evolution analysis. Green development is a system consisting of society, the economy, and the natural environment, but few studies have analyzed its evolutionary mechanisms from the perspective of a complex system. In previous studies, the use of information entropy and dissipative structure theory in the urban ecosystem (Lin & Xia, 2013), utilization and protection of cultivated land resources (Xun, Liu, & Wu, 2007), land use structure (Zhao, Xu, Mei, Wu, & Zhou, 2004), and urban landscape patterns (Antropy, 2004) made the application of information entropy and dissipative structure theory in the green development system possible. Based on the information entropy model and dissipative structure theory, this study establishes an evaluation index system, which includes a sustaining input index, imposed output index, destructive metabolism index, and regenerative metabolism index, to study the change in the entropy of the system of green development in China. The green development level of each province and city is calculated via integrated weighting methods. Our discussion focuses on temporal and spatial analyses and directly determines the main influencing factors. Subsequently, we put forward some valuable suggestions for the government.

2.1. Study area China is the largest developing country, encompassing 31 mainland provinces, as well as the Hong Kong, Macau, and Taiwan districts. China’s total area of land is 9.6 million km2, accounting for about 1/15 of the world’s total land area. On the whole, China is rich in resources but has a large population and a fragile ecological environment. Thus, the per capita strategic resources that can support development, such as arable land, water, iron ore, coal, petroleum, and natural gas, is far below the global average (SDESDRBBNU et al., 2015). In addition, the natural resources of China are distributed unevenly. Compared with the abundant natural resources in western regions, the resources in most of the eastern coastal areas are limited, and these areas are faced with resource shortages. Since the implementation of the “reform and opening up” policy, China’s rapid economic development has made major achievements with a milestone in the history of world economic development of the GDP ranking second in the world for several years. However, the extensive development mode of high growth, high energy consumption, and high emissions has put increasing pressure on the ecological environment. For example, air pollution problems in Beijing, the capital of China, have seriously affected the competitiveness of the city and its national image. According to the national monitoring of air quality of 161 prefecture-level and above cities, 16 cities met the average annual air quality standards, while the urban air quality of 145 cities exceeded the normal standard (SDESDRBBNU et al., 2015). China is not only short on water, but water pollution is also very serious. As a large developing country in the pursuit of modernization, China has faced numerous ecological dilemmas and neglected ecological and environmental problems. For example, there are more than 150 million acres of cultivated land pollution, and more than 40% of cultivated land is degraded. The soil erosion area accounts for nearly one third of the land area, and serious degradation of the forest ecosystem, land desertification, and rocky desertification pose threats to people’s lives and property. More urgent is that China has long been in the low end of the global value chain, with the most highly polluting, energy-consuming industries. Historical environmental problems have not been resolved, and the new environmental problems have followed. Therefore, concerning China’s current problems, the implementation of a green development strategy is needed. Owing to a lack of data availability, the objects of research in this paper include 30 provinces and cities in mainland China, excluding Tibet. 2.2. Methods The green development system is similar to the urban ecosystem, which is composed of economic, social, and ecological environments. They are of the dissipative structure, which must obtain materials and energies from the outside world and continuously produce products and wastes to maintain a stable and orderly state. At the same time, the system is like a complex organism with constant metabolism that can achieve system optimization and regeneration (Zhang, Yang, & Li, 2006). According to the dissipative structure, the entropy of the system can be divided into two parts. The first is entropy flow (△eS), which is produced when the socioeconomic system exchanges material and energy with the external environment and which can express the coordination of the system. The other is entropy production (△iS), which is generated in the socioeconomic system owing to the degradation of the internal environment and the construction of the eco-environment (Depew & Weber, 1988) and which can express the vigor of the system. The total entropy change (△S) is the green development status of the overall system and can describe the system as orderly and healthy. The formula can be expressed as follows:

ΔS = ΔeS + ΔiS. 53

(1)

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Fig. 1. The methods flow chart.

Our methods flow chart is as follows; due to space constraints, only some of the methods are detailed below (Fig. 1):

T = G1 + G2 + G3 + G4,

where G1, G2, G3, and G4 are four types of scores, and T is the total score, which represents the level of green development: the greater the score, the higher the level of green development.

2.2.1. Measurement models based on information entropy Entropy information reflects the degree of disorder of the system and is used to determine the direction of the evolution of the system (Shannon, 1948). According to it, in a system with uncertainty, if a random variable (X) is used to express the state of the system, set X｛x1, x2, …, xn｝(n≥2). The corresponding probability for each value of X is P =｛p1, p2, …,pn｝(0 ≤ pi ≤ 1, i = 1, 2, …, n), and ∑pi = 1. The information entropy can be described as follows:

2.3. Indicators In this study, the selection of indicators follows the representative, hierarchical, and operational principles. We establish an indicator system including the three dimensions of the economic, social, and ecological environments, which comprise two subsystems (socio-economic and natural ecological environmental subsystems) for evaluating the level of the green development. Furthermore, in order to assess the dynamic trends and evolution of the green development system, we divide the two subsystems of specific indicators into four categories based on the four basic processes of system evolution: the sustaining input indicators (ΔeS1), the imposed output indicators (ΔeS2), the regenerative metabolism indicators (ΔiS1), and the destructive metabolism (ΔiS2).

n

S (X ) = − ∑ pi ln pi ,

(2)

i=1

where S(X) is the information entropy of an uncertainty system, and pi is the probability of the random state variable Xi in the uncertainty system. Then, annual statistics are used to calculate △eS and △iS. The formula can be expressed as follows:

ΔS = −

1 ln m

n

∑ i=1

Pij Pj

ln

Pij Pj

, (3)

(1) Sustaining input indicators provide the material and energy for the green development system. They include per capita gross regional product (S1); per capita water resources (S2); forest coverage rate (S3); proportion of wetlands in total area of territory (S4); and indicators representing the level of economic development, such as labor productivity of the primary industry (S5), proportion of irrigated area of total sown area (S6), labor productivity of the secondary industry (S7), labor productivity of the tertiary industry (S8), and proportion of the tertiary industry added value (S9). (2) Imposed output indicators represent the consumption of natural resources and the pressure on social development through production and consumption by human beings. Energy consumption per unit of GDP (I1), water use per 10,000 yuan of industrial added value (I2), and per capita electricity consumption (I3) indicators represent the degree of pressure and threats to resources and the environment. Natural growth rate (I4), consumption of chemical fertilizer per unit of sown area (I5), amount of pesticide used per unit of sown area (I6), unemployment rate in urban areas (I7), per capita consumption expenditures of urban households (I8), and per capita consumption expenditures of rural households (I9) represent the pressures exerted by human life on social development. (3) Regenerative metabolism indicators: For a reduction of pollution, most wastes must decompose or be manually processed in the green development system. The indicators involved in waste treatment and sewage purification include wastewater treatment rate (R2) and ratio of industrial solid wastes utilized (R5). In addition, a better ecological environment functions to regulate the climate and purify the air, and there are significant benefits to improving the green function; thus, city municipal construction indicators are also

where ΔS represents the four types of entropy: sustaining input entropy (△eS1), imposed output entropy (△eS2), regenerative metabolism entropy (△iS1), and destructive metabolism entropy (△iS2). The parameter i (i = 1,2,…,n) represents an indicator, and j (j = 1,2,…,m) represents a year. Year-based S can be expressed, in which Xij is the value of the indicator i for year j, Pij is the standardized value of Xij calculated n from raw data, and Pj = ∑i = 1 Pij . According to the four types of evaluation indicators, the calculation formulae for entropy flow, entropy production, and total entropy change can be formed as follows:

⎧ ΔeS = ΔeS2 − ΔeS1 ΔiS = ΔiS2 − ΔiS1 , ⎨ ⎩ ΔcS = (ΔeS2 − ΔeS1) + (ΔiS2 − ΔiS2)

(4)

where △cS represents the total entropy change. 2.2.2. Assessment model of the level of green development with integrated weighting methods This study applies multi-objective decision-making methods, the analytic hierarchy process (AHP), and entropy to lessen the drawbacks caused by the weighting of the subjective and objective weights. The optimization of index weight is realized using the least squares method; for details of the calculation process of weights (wi), please refer to the reference (Qin, Sun, & Zou, 2015). The evaluation model of the green development level is as follows: n

G=

∑ wi Xij , i=1

(6)

(5) 54

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Table 1 Indicators system for assessing the level of green development. Criterion

Sub-criterion

Indicator type

Indicator

Unit

AHP

Entropy

Integrated

Socio-economic

Entropy flow (△eS)

Sustaining input (△eS1)

S1 S2 S3 S4 S5 S6 S7 S8 S9 I1 I2 I3 I4 I5 I6 I7 I8 I9

yuan m3/person % % ×103yuan % % % % tce/104yuan cu.m/104yuan wh/person ‰ ×104tons ton % yuan yuan

0.0301 0.0451 0.0451 0.0260 0.0127 0.0127 0.0190 0.0307 0.0288 0.0392 0.0392 0.0392 0.0203 0.0128 0.0128 0.0223 0.0322 0.0322

0.0736 0.1115 0.0467 0.1306 0.0588 0.0450 0.0610 0.0699 0.0311 0.0035 0.0051 0.0060 0.0170 0.0192 0.0078 0.0070 0.0061 0.0079

0.0530 0.1000 0.0325 0.1256 0.0377 0.0249 0.0548 0.0560 0.0421 0.0141 0.0145 0.0149 0.0155 0.0136 0.0089 0.0190 0.0134 0.0137

R1 R2 R3 R4 R5 R6 R7 R8 D1 D2 D3 D4 D5 D6 D7 D8 D9

unit % % sq.m % % % % ton/104 person ton/104 person ton/104 person ton/108 yuan ton/108 yuan ton/108 yuan ×104tons ×104tons ×104tons

0.0305 0.0305 0.0305 0.0305 0.0214 0.0513 0.0196 0.0357 0.0252 0.0252 0.0252 0.0413 0.0413 0.0413 0.0180 0.0164 0.0164

0.0298 0.0190 0.0114 0.0249 0.0226 0.0504 0.0488 0.0261 0.0104 0.0069 0.0139 0.0022 0.0024 0.0075 0.0041 0.0060 0.0057

0.0332 0.0179 0.0171 0.0260 0.0235 0.0485 0.0462 0.0220 0.0132 0.0119 0.0152 0.0138 0.0141 0.0162 0.0087 0.0094 0.0091

Imposed output (△eS2)

Natural ecological environmental

Entropy production (△iS)

Regenerative metabolism (△iS1)

Destructive metabolism (△iS2)

development, on the other hand, does not use vector quantization for different types of indicators, and the data processing must distinguish between positive and negative indicators, according to their attributes. This study uses the standard deviation of data processing. Of the indicators of the green development level, the sustaining input and regenerative metabolism indicators are positive, and the imposed output and destructive metabolism indicators are negative. The standardization methods of the positive and negative indicators are as follows:

included. They include numbers of public transportation vehicles per 10,000 people (R1), green coverage rate of the built district (R3), public recreational green space per capita (R4), percentage of nature reserves in the region (R6), proportion of total investment in the treatment of environment pollution in the GDP (R7), and percentage of the rural population with access to clean water (R8). (4) Destructive metabolism indicators: The destruction of the ecological environment is caused by waste from human production and life. Such indicators include per capita sulfur dioxide emissions (D1), per capita chemical oxygen demand (COD) emissions (D2), per capita ammonia nitrogen emissions (D3), sulfur dioxide emissions per unit of GDP (D4), COD emissions per unit of GDP (D5), ammonia nitrogen emissions per unit of GDP (D6), industrial solid waste production (D7), consumption wastes from urban households (D8), and total water discharged (D9) (Table 1).

(1) Positive indicators:

Xij′ = [Xij − Min (Xi )] [Max (Xi ) − Min (Xi )]

(7)

(2) Negative indicators:

Xij′ = [Max (Xi ) − Xij ] [Max (Xi ) − Min (Xi )]

(8)

2.4. Data sources and processing 3. Results 2.4.1. Data sources The data sources for this paper include the China Statistical Yearbook (2001–2015), the China Statistical Yearbook On the Environment (2001–2015), the China City Statistical Yearbook (2001–2015), and the China Energy Statistical Yearbook (2001–2015).

3.1. Entropy change analysis of the green development system From the above formulae, we can determine the sustaining input entropy, regenerative metabolism entropy, imposed output entropy, and destructive metabolism entropy and then calculate the entropy flow, entropy production, and total entropy change.

2.4.2. Data processing This research deals with two issues: an entropy change analysis of the evolution of the green development system and the level of green development. The entropy change analysis uses the information entropy model for vector quantization, which only normalizes the original data of each index without distinguishing between the positive and negative indexes. The assessment model for the level of the green

3.1.1. Sustaining input entropy and imposed output entropy From 2000 to 2014 (Table 2), in most provinces and cities, the sustaining input entropy showed an upward trend. From 2000 to 2009, the sustaining input entropy increased rapidly, and demand increased on the natural environment for materials and energy owing to the 55

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Table 2 Sustaining input entropy and imposed output entropy of the green development system.

Table 3 Regenerative metabolism entropy and destructive metabolism entropy of the green development system.

S/I

2000

2005

2010

2014

R/D

2000

2005

2010

2014

Beijing Tianjin Hebei Shanxi Neimenggu Liaoning Jilin Heilongjiang Shanghai Jiangsu Zhejiang Anhui Fujian Jiangxi Shandong Henan Hubei Hunan Guangdong Guangxi Hainan Chongqing Sichuan Guizhou Yunnan Shaanxi Gansu Qinghai Ningxia Xinjiang

0.28/0.32 0.30/0.33 0.27/0.31 0.28/0.28 0.33/0.26 0.31/0.32 0.30/0.29 0.29/0.26 0.30/0.32 0.28/0.31 0.29/0.33 0.26/0.29 0.29/0.30 0.25/0.27 0.29/0.31 0.25/0.29 0.29/0.31 0.27/0.31 0.31/0.32 0.27/0.29 0.31/0.28 0.28/0.31 0.28/0.29 0.19/0.25 0.26/0.26 0.27/0.29 0.26/0.26 0.23/0.25 0.25/0.27 0.26/0.26

0.32/0.31 0.33/0.32 0.31/0.30 0.28/0.33 0.26/0.35 0.32/0.33 0.29/0.33 0.26/0.32 0.32/0.32 0.31/0.31 0.33/0.32 0.29/0.29 0.30/0.31 0.27/0.28 0.31/0.32 0.29/0.29 0.31/0.32 0.31/0.29 0.32/0.33 0.29/0.30 0.28/0.31 0.31/0.32 0.29/0.31 0.25/0.27 0.26/0.29 0.29/0.31 0.26/0.33 0.25/0.26 0.27/0.31 0.26/0.29

0.32/0.31 0.32/0.32 0.32/0.33 0.33/0.33 0.32/0.32 0.33/0.32 0.33/0.33 0.34/0.31 0.33/0.33 0.33/0.33 0.33/0.33 0.32/0.33 0.32/0.33 0.31/0.33 0.33/0.33 0.30/0.32 0.34/0.33 0.33/0.33 0.33/0.32 0.31/0.33 0.33/0.31 0.32/0.34 0.33/0.33 0.31/0.32 0.30/0.33 0.32/0.31 0.34/0.33 0.31/0.3 0.33/0.31 0.31/0.31

0.32/0.29 0.32/0.30 0.32/0.33 0.33/0.33 0.32/0.30 0.32/0.31 0.32/0.30 0.34/0.29 0.33/0.32 0.33/0.32 0.33/0.32 0.33/0.33 0.33/0.33 0.32/0.33 0.33/0.32 0.32/0.32 0.34/0.33 0.33/0.33 0.34/0.32 0.33/0.33 0.33/0.32 0.33/0.32 0.34/0.32 0.33/0.33 0.32/0.33 0.31/0.31 0.32/0.33 0.33/0.30 0.32/0.31 0.31/0.32

Beijing Tianjin Hebei Shanxi Neimenggu Liaoning Jilin Heilongjiang Shanghai Jiangsu Zhejiang Anhui Fujian Jiangxi Shandong Henan Hubei Hunan Guangdong Guangxi Hainan Chongqing Sichuan Guizhou Yunnan Shaanxi Gansu Qinghai Ningxia Xinjiang

0.32/0.32 0.33/0.33 0.30/0.30 0.26/0.28 0.30/0.31 0.33/0.33 0.32/0.33 0.33/0.33 0.30/0.32 0.32/0.32 0.31/0.31 0.30/0.29 0.30/0.31 0.29/0.30 0.32/0.32 0.30/0.31 0.30/0.31 0.31/0.31 0.31/0.30 0.31/0.32 0.30/0.31 0.32/0.32 0.33/0.32 0.30/0.31 0.31/0.32 0.30/0.31 0.30/0.29 0.28/0.29 0.27/0.27 0.32/0.33

0.32/0.33 0.33/0.32 0.31/0.31 0.32/0.32 0.32/0.32 0.33/0.33 0.33/0.33 0.33/0.33 0.32/0.32 0.32/0.32 0.32/0.31 0.31/0.31 0.32/0.31 0.32/0.32 0.32/0.32 0.32/0.32 0.32/0.32 0.32/0.32 0.32/0.31 0.32/0.32 0.31/0.32 0.33/0.33 0.33/0.32 0.32/0.32 0.32/0.32 0.31/0.31 0.32/0.31 0.3-/0.31 0.32/0.33 0.32/0.32

0.32/0.32 0.32/0.33 0.31/0.31 0.32/0.32 0.33/0.33 0.32/0.33 0.33/0.32 0.33/0.33 0.30/0.31 0.31/0.32 0.31/0.30 0.31/0.32 0.31/0.31 0.32/0.32 0.32/0.32 0.30/0.31 0.32/0.32 0.32/0.31 0.33/0.32 0.32/0.33 0.32/0.32 0.33/0.33 0.32/0.32 0.31/0.31 0.32/0.32 0.33/0.32 0.33/0.33 0.32/0.33 0.33/0.33 0.32/0.32

0.33/0.32 0.33/0.33 0.31/0.30 0.33/0.26 0.33/0.30 0.32/0.33 0.33/0.32 0.33/0.33 0.31/0.30 0.32/0.32 0.31/0.31 0.32/0.30 0.31/0.30 0.32/0.29 0.32/0.32 0.31/0.30 0.32/0.30 0.32/0.31 0.31/0.31 0.32/0.31 0.31/0.30 0.32/0.32 0.33/0.33 0.33/0.30 0.32/0.31 0.32/0.30 0.34/0.30 0.33/0.28 0.33/0.27 0.33/0.32

development of the social economy. After 2009, with the world economic crisis, China’s economic growth slowed and reached a period of industrial structure adjustment. The sustaining input entropy growth rate was relatively slow, and the demand of the social economic system placed on the natural ecological system was controlled to a certain degree. Different regions showed different characteristics of imposed output entropy. In the developed regions, such as Beijing, Tianjin, Shanghai, Jiangsu, Zhejiang, and Guangdong, the imposed output entropy appeared to decrease slowly, while other regions showed a gradual increase, suggesting that the pressure of social economic activities on the natural ecosystem increased in some provinces and cities. In general, during the study period, although the sustaining input entropy increased, the imposed output entropy and the pressure on the natural resource system increased, as well.

coordination of the system remains need to be improved. The entropy flow of the other provinces and cities was smaller than zero, and the coordination of the system developed in a good direction. Almost all provinces’ and cities’ entropy production were smaller than zero, the vitality of the green development system was improved. Fig. 2 showed that, from 2000 to 2014, the number of provinces and cities for which the total entropy change was less than 0 increased from 7 to 25, indicating that the order of the green development system has been enhanced. In 2007, the Seventeenth Congress of the Communist Party of China put forward for the first time the goal to “vigorously promote the construction of ecological civilization.” The effect of the introduction of this strategy and the support of local government were significant. The system of green development grew in an orderly and healthy manner in most provinces and cities of China.

3.1.2. Regenerative metabolism entropy and destructive metabolism entropy Over the past 15 years, the regenerative metabolic entropy almost in all provinces and cities showed a slowly increasing trend with fluctuations within a narrow range, indicating that the urban infrastructure had been greatly improved and environmental pollution was effectively controlled. The destructive metabolic entropy of Anhui, Guangxi, Guizhou, Yunnan, Gansu, and Qinghai was on the rise, and the economic development and the industrialization levels in these areas were low. Thus, the solid waste, gas, wastewater, and other substances generated through production and human life increased year by year, while the destructive metabolic entropy in the rest of the provinces and cities appeared to decrease. This finding suggests that, alongside an enhanced awareness of environmental protection, the total amount of pollution discharged from human life and production had decreased (Table 3).

3.2. Evaluation of the level of green development From 2000 to 2014, the sustaining input index scores increased, showing that the supporting function of the natural ecosystem for the social economic system in China has gradually increased. With the rapid development of the social economy, the imposed output index scores decreased slowly while fluctuating, demonstrating that human activities exerted increasing stress on the natural ecosystem. Thus, the diversity and complexity of the system were boosted. The regenerative metabolic index scores increased owing to the improvement of municipal infrastructure construction and the increased investment in environmental protection, and the regenerative metabolic function of the ecological system was enhanced. The destructive metabolism index scores increased at first, declined, and eventually increased again, indicating that destructive ecosystem metabolic pressure decreased first and then increased. Overall, the level of green development of 30 provinces and cities in China (Table 5) exhibited a steady upward trend from 2000 to 2014. However, the level of green development was not high. The average level of green development in nearly all of the

3.1.3. Entropy flow, entropy production, and total entropy change The entropy flow (Table 4) of Hebei, Shanxi, Anhui, Fujian, Jiangxi, Guangxi, Guizhou, Yunnan, and Xinjiang was greater than zero, and the 56

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Table 4 Entropy flow and entropy production of the green development system. Region

Beijing Tianjin Hebei Shanxi Neimenggu Liaoning Jilin Heilongjiang Shanghai Jiangsu Zhejiang Anhui Fujian Jiangxi Shandong Henan Hubei Hunan Guangdong Guangxi Hainan Chongqing Sichuan Guizhou Yunnan Shaanxi Gansu Qinghai Ningxia Xinjiang

Entropy flow

Region

2000

2005

2010

2014

0.03 0.03 0.04 0 −0.07 0 0 −0.03 0.02 0.04 0.04 0.03 0.01 0.02 0.02 0.04 0.03 0.04 0.01 0.02 −0.03 0.02 0.02 0.05 0 0.03 0 0.03 0.02 0

0.02 −0.01 0.02 −0.02 −0.05 −0.03 −0.01 −0.02 0.01 0.02 0.02 0.02 0.02 0.04 0 0.02 0 0.03 0 0.02 −0.01 0 0 0.01 0.01 −0.01 −0.01 0.03 −0.02 −0.01

−0.01 −0.01 0.01 0 −0.01 −0.01 0 −0.02 0 0 0 0.01 0 0.02 0 0.01 0 0.01 −0.01 0.01 −0.03 0.02 0 0.01 0.02 −0.01 −0.01 −0.01 −0.01 0

−0.03 −0.03 0.01 0.01 −0.02 −0.02 −0.01 −0.04 −0.01 −0.01 −0.02 0.01 0 0 −0.01 −0.01 −0.01 −0.01 −0.02 0 −0.02 −0.01 −0.02 0 0 0 0.01 −0.03 −0.01 0.01

Beijing Tianjin Hebei Shanxi Neimenggu Liaoning Jilin Heilongjiang Shanghai Jiangsu Zhejiang Anhui Fujian Jiangxi Shandong Henan Hubei Hunan Guangdong Guangxi Hainan Chongqing Sichuan Guizhou Yunnan Shaanxi Gansu Qinghai Ningxia Xinjiang

Entropy production 2000

2005

2010

2014

−0.01 −0.02 0.05 0.07 0.02 0.02 −0.01 −0.01 0.02 0.02 0.01 0.01 0.04 0.03 0.04 0.03 0.02 0.02 0.01 0 −0.05 0 0.01 −0.04 0 0.02 −0.03 −0.02 0.02 0

−0.01 −0.05 0.03 0.02 −0.02 0 0 0 −0.01 0 0.01 0.03 0.01 0.03 0.01 0.02 0.01 0.02 −0.02 0 −0.05 0 0.02 −0.04 0.01 0.03 −0.01 0 −0.03 0.01

−0.1 −0.05 −0.02 0 −0.04 0 0 0 −0.01 −0.03 −0.02 0.02 0.02 0.03 −0.03 0.01 0.01 0.02 −0.07 0.01 −0.05 0 0.02 −0.01 0 0.01 −0.01 −0.03 −0.05 0.01

−0.09 −0.06 −0.01 −0.01 −0.03 −0.01 −0.04 −0.04 −0.05 −0.03 −0.03 0 −0.02 −0.01 −0.01 0.01 −0.02 −0.01 −0.05 0 −0.09 −0.02 −0.01 0 0.01 0 −0.01 −0.04 −0.06 −0.02

provinces and cities was less than 0.5, revealing a great potential for improvement.

slightly bimodal distribution in 2014. This bimodal model reveals a polarization trend in the level of green development in China. In addition, the level of green development in the provinces and cities tends to disperse. This gap increased not because of the polarization between the high and low green development levels, but because the green development in provinces and cities with a moderate level increased, and they transitioned into the high-level group. The proportion of provinces and cities with a high level of green development grew, while the proportion of provinces and cities with a low level of green development gradually decreased. The peak of the green development level is spike-shaped. Compared with that in 2000, the kurtosis in 2007 was higher, and the gap in the green development level between each province and city was large. By 2014, the kurtosis was lower than in 2007, revealing the shrinking of this gap (Fig. 4).

4. Discussion

4.2. Spatial patterns in the green development level

4.1. Temporal analysis of the green development level

In terms of space, there are some differences in green development between the different regions, with a ladder-like distribution. The evaluation of the green development indicators revealed a similar feature to the original economic regionalization; that is, the less-developed areas in China are not only in a disadvantaged absolute level of economic development but are also lagging behind in their development model (Zeng & Bi, 2014). High-value areas are concentrated in the economically developed coastal areas and other western regions with rich resources, such as Qinghai and Neimenggu. Three provinces in Northeast China exhibit a medium level of green development, while Southwest Yunnan, the Guizhou region, and the central regions exhibit a relatively low, below-average level. For a clearer view of these differences, the levels of green development of 30 provinces and cities in China were divided into four grades using the ISODATA method (Sun & Lin, 1999): high, medium-high, medium-low, and low levels. From analyses of the scores of the sustaining input index, imposed output

Fig. 2. Cartogram of the total entropy change of 30 provinces and cities from 2000 to 2014.

Kernel density estimation is a non-parametric test method that can be used to estimate the unknown density function in the theory of probability (Zhang, Cheng, & Guo, 2012). We can use it to obtain the distribution map of the green development levels of 30 provinces and cities in China from 2000 to 2014 (Fig. 3). The diagram shows the kernel density distributions in three different years (2000, 2007, and 2014), and it can roughly explain the evolution of the level of the green development. The center of the curve of distribution of green development in China’s provinces and cities turned to the right from 2000 to 2014, representing different degrees of improvement. The proportion of low green development level was smaller than that of the high level. The whole region formed a rapid and comprehensive development pattern with unimodal distribution in 2000 and 2007 that transformed into 57

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Table 5 The green development level of 30 provinces and cities in China from 2000 to 2014. Region

2000

2002

2004

2006

2008

2010

2012

2014

Mean

Beijing Tianjin Hebei Shanxi Neimenggu Liaoning Jilin Heilongjiang Shanghai Jiangsu Zhejiang Anhui Fujian Jiangxi Shandong Henan Hubei Hunan Guangdong Guangxi Hainan Chongqing Sichuan Guizhou Yunnan Shaanxi Gansu Qinghai Ningxia Xinjiang

0.405 0.381 0.284 0.250 0.267 0.299 0.294 0.307 0.446 0.335 0.345 0.274 0.344 0.289 0.307 0.262 0.285 0.297 0.334 0.272 0.350 0.279 0.277 0.236 0.316 0.269 0.243 0.357 0.225 0.337

0.431 0.398 0.299 0.262 0.283 0.321 0.316 0.322 0.475 0.345 0.366 0.283 0.337 0.312 0.317 0.273 0.290 0.310 0.332 0.306 0.349 0.305 0.288 0.250 0.321 0.284 0.260 0.376 0.247 0.356

0.446 0.410 0.311 0.288 0.322 0.344 0.336 0.341 0.503 0.368 0.377 0.299 0.353 0.316 0.329 0.292 0.301 0.314 0.341 0.299 0.356 0.309 0.330 0.269 0.340 0.292 0.290 0.391 0.294 0.360

0.475 0.425 0.329 0.303 0.346 0.350 0.348 0.354 0.505 0.389 0.385 0.304 0.375 0.337 0.365 0.299 0.320 0.328 0.357 0.316 0.372 0.334 0.334 0.282 0.342 0.310 0.305 0.396 0.286 0.364

0.497 0.452 0.355 0.338 0.391 0.380 0.382 0.387 0.538 0.424 0.434 0.338 0.397 0.362 0.397 0.319 0.348 0.354 0.395 0.345 0.402 0.368 0.362 0.318 0.363 0.340 0.323 0.446 0.333 0.382

0.515 0.466 0.386 0.363 0.424 0.416 0.426 0.421 0.541 0.447 0.449 0.355 0.431 0.409 0.412 0.333 0.375 0.380 0.453 0.374 0.439 0.406 0.377 0.348 0.386 0.376 0.347 0.457 0.356 0.409

0.552 0.503 0.387 0.381 0.455 0.452 0.431 0.438 0.560 0.461 0.466 0.363 0.436 0.431 0.425 0.337 0.387 0.386 0.433 0.396 0.445 0.425 0.395 0.375 0.384 0.404 0.382 0.492 0.372 0.432

0.586 0.541 0.389 0.379 0.478 0.442 0.445 0.461 0.599 0.492 0.501 0.392 0.452 0.418 0.441 0.353 0.414 0.404 0.450 0.406 0.441 0.447 0.407 0.403 0.396 0.420 0.400 0.491 0.393 0.462

0.487 0.447 0.343 0.320 0.370 0.374 0.370 0.379 0.523 0.409 0.414 0.326 0.390 0.357 0.375 0.308 0.339 0.344 0.385 0.337 0.395 0.361 0.347 0.309 0.357 0.335 0.320 0.429 0.310 0.386

such as wastewater and solid pollutants, has been produced, reducing the function of the natural environment. During rapid development, governments should focus on environmental protection so that the development of subsystems is coordinated. 4.2.2. Medium-high level of green development This grade includes the regions of Zhejiang, Jiangsu, Qinghai, and Neimenggu. The levels of green development in Qinghai and Neimenggu are also high, a finding similar to those of previous studies (SDESDRBBNU et al., 2015; Dai, 2015). The natural environmental conditions in Qinghai and Neimenggu are better, and the population is relatively small. The carrying capacity of resources and the environment is relatively strong, and the reduction metabolic scores of these provinces and cities were also higher than in other regions. In recent years, Qinghai and Neimenggu have improved their living standards and reinforced ecological protection. As a result of the national government’s strong support, these areas of policy have received greater effort. The levels of green development in Jiangsu and Zhejiang are similar. The sustaining input scores and reduction metabolic scores are lower than those of high level areas. Because of environmental deterioration, the imposed output scores and destructive metabolism scores of Jiangsu and Zhejiang were very low. They, therefore, should adopt complementary measures and regional cooperation to improve their level of green development.

Fig. 3. Kernel density distribution map of the green development level of 30 provinces and cities in China.

index, reduction metabolic index, and destructive metabolism index, we can clearly see the existing problems for the 30 provinces and cities and furthermore provide a reasonable basis for the government to formulate policies according to the analysis. 4.2.1. High level of green development This grade includes the regions of Beijing, Shanghai, and Tianjin. A comparison of each subsystem scores is shown in Fig. 5. The sustaining input scores of these provinces and cities are higher than other subsystem scores. Beijing, Shanghai, and Tianjin are located in eastern China, which are the most dynamic regions in terms of economic growth, and these cities have diversified economic and trade activities, powerful comprehensive strength, and leading economies. The structures of their primary, secondary, and tertiary industries are reasonable, and the processes of industrialization and urbanization have been relatively accelerated. The imposed output scores and destructive metabolism scores of these regions are low. Owing to the development of human life and production, along with great consumption of natural resources, however, there have been increasing pressures on environment. Through production and human life, a large amount of waste,

4.2.3. Medium-low level of green development This grade includes the regions of Xinjiang, Heilongjiang, Fujian, Guangdong, Chongqing, Jilin, Liaoning, Hainan, and Shandong. Heilongjiang, Liaoning, and Jilin are located in northeastern China. They are abundant with natural resources; thus, their sustaining input scores are not very low. The low scores in these heavily industrial provinces are caused by substantial waste generated production. Owing to weaker policy support and imperfect social infrastructure, the reduction metabolic scores are not high. Hainan, Fujian, Shandong, and Guangdong fall into the medium-low level of green development in the 58

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Fig. 4. Spatial patterns of the levels of green development of 30 provinces and cities in China.

4.2.4. Low level of green development Cities and provinces with a low level of green development account for a large proportion. This grade includes the regions of Shaanxi, Jiangxi, Hubei, Sichuan, Guangxi, Hunan, Guizhou, Gansu, Yunnan, Ningxia, Anhui, Hebei, Shanxi, and Henan. With an overall lack of advantages, the four subsystems scores of these provinces and cities are very low. From a geographical perspective, the low-level areas are mainly concentrated in the central and western regions of China, possibly because of the level of economic development or the degree of defective social infrastructure in these regions. The scores of the

coastal areas. Their imposed output scores are very low, and their sustaining input scores are also not high. The reduction metabolic scores of Xinjiang and Chongqing are higher than those of most areas, and governmental policy support is high. Unlike cities and provinces with high levels of green development, those with medium-low levels should actively use the radiation effects of high-level areas in future development. They should explore the limitations of the development of shortage factors and narrow the gap with the high-level areas.

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Fig. 5. Each subsystem score of 30 provinces and cities of China in 2014.

Thirdly, in the central and most of the western regions, there are more serious problems, such as a lack of resources, relatively delayed economic development, and low government support. The government should increase base infrastructure construction and investment in environmental restoration. The government must take measures to encourage enterprises to actively adopt resource-saving and environmentally friendly development measures. At the same time, the economically developed areas should exert a radiation effect so as to promote the development of less-developed areas. These areas should reduce regional differences and achieve more uniform development. The global focus on green development is a good opportunity for developing countries, whose rapid industrialization and development requires high energy consumption that causes environmental destruction. Most developing countries prioritize development before the environment and focus on automation to enhance productivity. Challenges like these have placed substantial pressure on policymakers. As European countries transform their energy systems, the new technologies will benefit the fight against climate change, accelerate a green economy, and encourage other countries to make progress in green technological industries and economies. The success of Europe in climate change initiatives has important implications for the strategic development of Asian countries (Tsai et al., 2016). Sustainable development depends on maintaining ecosystem services. The key to sustainable development is being measured in the exploitation of resources for socio-economic development (Sun, Zhang, Zou, Li, & Qin, 2015). Sustainable development is the responsibility of all parts of society, government, public interest groups, consumers, and the private sector (Holthus, 1999). Stakeholders, including citizens and firms, gain influence in public management and scientific support, and can play an important role in transition towards increased future sustainability (Soma, Onwezen, Salverda, & Dam, 2016). Stakeholders have a role through transition towards food secure green and liveable cities (Soma, Dijkshoorn-Dekker, & Polman, 2018). Principles such as stakeholder consultation should inform green development strategies in China. This study has the following limitations. Green development is a complex system comprising society, the economy, and the natural environment. Moreover, evolution analysis requires a long-term perspective, while the data currently available are short-term. In addition, the conclusion of this paper is based on provincial-level data. If we use county-level data, we can further analyze the spatial and temporal differences in green development.

sustaining input indicators and the reduction metabolic indicators are lower than those of the other two subsystems, especially in Guizhou, Gansu, Shaanxi, Henan, and Anhui. There are tremendous differences among other regions, and they have great potential for increase. Although Hebei and Guangxi are in the coastal region, their level of green development is very low. Hebei is an economically less-developed province in this area; thus, its sustaining input score is very low. Compared with Hebei, Guangxi has low reduction metabolic scores. In future development, government should focus on overall planning, actively develop the economy, and invest more in social infrastructure facilities. 5. Conclusions Green development is a complex open system and analyzing the direction of its evolution is important. Using the information entropy model, we can do so. The green development levels of 30 provinces and cities in China can be measured with integrated weighting methods. From the temporal and spatial perspectives, we can analyze the causes of variation. The results reveal that the system entropy, entropy production, and total entropy generally exhibit downward trends, and the system moves in an orderly and healthy direction. The degree of order of the green development system has been strengthened, exhibiting a convergence trend along with a big gap in the study period. In terms of the spatial differences in the green development level, areas with a low level of green development account for a large proportion and are mainly concentrated in the central and most of the western region. The high-level areas are a minority and are mostly located in economically developed regions. Based on the results of the above analysis, in enhancing the degree of green development, narrowing the differences among regions is of great significance. Thus, this paper puts forward the following suggestions: First, the eastern region’s developed economy leads in per capita GDP, putting great pressure on the environment and causing great resource consumption. These areas include Xinjiang, Qinghai, and Neimenggu, which are rich in and have high per capita resources, with much land and few people. Therefore, they should continue to implement the green development strategy and take advantage of complementary cooperation among different regional policies. Secondly, the northeastern regions and the coastal areas are subdeveloped. Although the northeastern region has natural geographical advantages, abundant resources, and suitable climate, its economic development level is not high owing to its old industrial base. Thus, a transformation in the economic development mode and improvement in the level of economic development are needed. The coastal sub-developed areas should take advantage of their superior geographical position and should not ignore environmental protection in the pursuit of rapid economic development.

Author contributions Caizhi Sun, Yanli Tong, and Wei Zou designed the paper, and all coauthors collected the data, ran the calculations, and wrote the paper.

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Conflicts of interest

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