An empirical investigation of the coordinated development of natural resources, financial development and ecological efficiency in China

Resources Policy 65 (2020) 101580

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An empirical investigation of the coordinated development of natural resources, financial development and ecological efficiency in China Hashim Zameer a, Humaira Yasmeen a, *, Rong Wang b, Jing Tao a, Muhammad Nasir Malik c a

College of Economics and Management, Nanjing University of Aeronautics and Astronautics, 29 Jiangjun Avenue, Jiangning District, Nanjing, Jiangsu Province, 211106, China b Nanjing Institute of Technology, Nanjing, Jiangsu Province, 211106, China c UCP Business School, University of Central Punjab, Lahore, Pakistan

A R T I C L E I N F O

A B S T R A C T

Keywords: Coupling Natural resources Regions Financial development Ecological efficiency

The role of financial development, natural resources and ecological efficiency has generated many attractive avenues for scholarly research, but only few studies have evaluated the coupling coordination degree of natural resources, financial development and ecological efficiency in China from regional perspective. Taking a natural and socio-economic dynamics into accounts, this study explores the coupling coordination degree of natural resources, financial development and ecological efficiency using the data of three Chinese regions i.e. Eastern, Central and Western region from 2006 to 2018. We employed entropy weight method and coupling coordination degree evaluation method for data analysis. The findings of the study show that the financial development of the central and western regions was higher than that of the eastern region. Secondly, although the eastern region lacks natural resources, but its resource utilization efficiency is high. In contrast, the central and western regions are rich in resources, but the utilization efficiency is low. It shows that the regions with relatively backward economic development are over-reliant on natural resources. Thirdly, the ecological efficiency in the central region is lower than that in the eastern region, but it is larger than that in the western region. Finally, the study concludes with some policy implications.

1. Introduction Since the reforms and opening up, Chinese people have enjoyed the benefits of rapid economic growth, but also have paid a cost in terms of environmental pollution: such as smog, water pollution and contami­ nated land events. According to the data of the Ministry of Ecology and Environment from “2017 China’s Ecological Environment Bulletin”, in terms of air pollution, 239 cities out of 338 prefecture-level cities have exceeded the air quality standards, exceeding the standard ratio of 70.70%. In terms of water pollution, based on the data of 5100 groundwater quality monitoring points, the monitoring points of poor and extremely poor water quality accounted for 51.8% and 14.8%, respectively. At the same time, nearly two-thirds of the groundwater quality was unqualified. In terms of land resources, the existing soil erosion area was 294.90 ha, accounting for 31.10%. Similarly, it can be stated that China’s environmental pollution is no longer limited to specific regions, specific areas, specific pollutants, but affects all aspects

of people’s daily lives. On the other hand, economic development has also brought about a large amount of resources while causing serious environmental pollution. According to the statistics of the Ministry of Environmental Protection, the Chinese Academy of Sciences and the World Bank (world development indicators), the annual indirect losses caused by environmental degradation (pollution) in China are about 10% of the national GDP Lixin and Zhenghao (2019). In recent years, the energy shortage problem has also increased with economic growth and external dependence, from 1.3% in 1990 to 18.4% in 2013, and with an increasing progress of the transportation industry, the energy shortage problem will be further highlighted (Chenglin and Xueping, 2016; Zaidi et al., 2019). Xie et al. (2018) also highlighted the significance of the transport sector in the context of energy and ecological efficiency. Excessive pursuit of economic progress and neglecting to protect of natural resources and ecosystem are the main reasons leading to the current resources and environmental issues in the country. In addition, shortage of the natural resources and the deterioration of the ecological

* Corresponding author. E-mail addresses: [email protected], [email protected] (H. Zameer), [email protected] (H. Yasmeen), [email protected] (R. Wang), [email protected] (J. Tao), [email protected] (M.N. Malik). https://doi.org/10.1016/j.resourpol.2020.101580 Received 28 November 2019; Received in revised form 24 December 2019; Accepted 2 January 2020 Available online 20 January 2020 0301-4207/© 2020 Elsevier Ltd. All rights reserved.

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environment have posed a great threat to the sustained and healthy development of society as well as China’s economy. Therefore, it is necessary to emphasize on vigorously developing a green and low-carbon economy from the coordinated development of natural re­ sources and the ecological environment of the country (Lixin and Zhenghao, 2019). Furthermore, financial development is also a key element in pro­ moting the construction of ecological civilization and improving ecological efficiency (Yigen and Kaiwen, 2018). Baloch et al. (2019) also found that financial development in the country improves its ecological footprints. The study of (Shahbaz et al., 2018a,b) emphasized on the role of financial development and innovations toward environmental degradation. Yasmeen et al. (2019) tried to determine the role of the government, firms and civil society for environmental sustainability. Zameer et al. (2019) highlighted the role of cleaner production tech­ nologies. In the process of China’s regional economic development, financial development is increasingly important for promoting eco­ nomic growth, promoting the optimization and upgrading industrial structure and improving the impact of ecological efficiency (Hao et al., 2016; Yiqing et al., 2017). On the one hand, financial development and ecological efficiency mutually promote mutual influence: financial development through the effect of capital allocation optimize regional industrial structure. Similarly, reasonable allocation of guiding funds improves ecological efficiency. In contrast, low ecological efficiency will increase the cost of economic activities and restrict financial develop­ ment (Dafermos et al., 2018). On the other hand, financial development promotes regional struc­ tural innovation and promotes eco-efficiency by promoting regional innovation capabilities (Yiqing et al., 2017). However, there are some typical problems in the development and financial practice of various regions in China. For example, the efficiency of capital utilization in some regions is low, the local government debt is high, and financial risks are prominent (Liang et al., 2017; Lyu and Shi, 2018). Let’s take another example, the financial development level in the central and western regions and rural areas is weak, and the capital support for regional development, especially environmental protection investment, is limited (Junjie and Zhaoxiang, 2013). In this context, the “greenness” of regional development with financial support has risen to the national strategy. Therefore, the comprehensive and coordinated development of natural resources, financial development and ecosystems have become a serious problem that needs to be addressed. However, existing literature mainly focuses on exploring the relationship between financial devel­ opment and ecological efficiency (Jingfeng, 2016; Qinghua et al., 2019; Yingying and Renxiang, 2017). The impact of natural resources on eco-efficiency (Chenglin and Xueping, 2016; Rong Wang et al., 2019; Yang and Zhang, 2018), but we could not go through any study which include financial development, natural resources and ecosystems in the same system and studies the interaction between the three from a sys­ temic perspective. Therefore, the core objective of this paper is to integrate financial development, natural resources and ecological efficiency into the same system to consider the coordinated development between them. In this study, we explored the systematic coordinated development between natural resources, financial development and ecological efficiency, the development model of financial development-natural resources and ecological efficiency is revealed, which provides a useful reference for the systematic development of natural resources, financial development and ecological efficiency. First, we construct an index system. Following this, we comprehensively analyzed the subsystems. The evaluation performed in this study provides roadmap for policy implications. Especially, the study provides policy support for the “greenness” of regional development with the support of financialization.

analysis and extensive research on the sustainable development of nat­ ural resources and environment. M. Song et al. (2019) believe that improving ecological efficiency is a significant mechanism to accom­ plish a green and sustainable development in the country. Shahbaz, Ozturk, et al. (2016) indicate that financial development lowers the environmental quality. The eco-efficiency index refers to maximizing the value of products and services under the conditions of minimizing resource consumption and environmental pollution emissions (Chenglin and Xueping, 2016; Yiqing et al., 2017). Among them, resource con­ sumption and environmental pollution emissions refer to the discharge of wastes from the use of resources, raw materials and energy. The concept ecological efficiency reflects the economic results and envi­ ronmental impacts of human economic activities. It is an effective concept and tool that can be used to measure sustainable development, including economic and resource environment factors (Fet, 2004). The review of existing literature indicates that scholars have carried out in-depth research on the relationship among eco-efficiency and financial development and the realization mechanism of improving ecoefficiency (Chen, 2015; Dagili� ute_ and Juknys, 2012; Moghadam and Dehbashi, 2018; Shahbaz et al., 2016a,b; J. Yang et al., 2015; Yin et al., 2019). Shahbaz et al. (2017) indicated that financial development re­ lates to economic growth. In another study, (Shahbaz et al., 2013a,b) focused on the dynamic linkages between trade, economy, financial development and energy. Further, (Shahbaz et al., 2013a,b) trade, economy, financial development and energy reinforces carbon emis­ sions. At present, relevant research mainly focused on two aspects: one is to explore the one-way impact of financial development on production efficiency and the impact mechanism from the perspective of financial agglomeration. For example, Jingfeng (2016) found that financial agglomeration has a spillover effect and plays a vital role in ecological efficiency. Overall, there is a positive spatial correlation between China’s financial agglomeration and regional eco-efficiency. However, there are also differences based on the local area. The accumulation of the securities industry and the banking sector has improved the ecological efficiency of the central and eastern regions. Whereas, the development in financial density has improved the ecological efficiency of the western region (Yiqing et al., 2017). Yuan et al. (2019) explored the mechanism of the impact of financial agglomeration on regional green development. The study found that financial agglomeration plays a vital role to enhance green development in the Western region of China, whereas it has spatial spillover effects in Eastern and Central of China. Gokmenoglu and Rustamov (2019) highlighted that natural resources abundance positively affect financial development. Sinha and Sengupta (2019) highlighted the role of natural resources rent toward human development. Scholars have also empha­ sized on the role of the resource abundance toward various economic factors (Ahmed et al., 2016; Shahbaz et al., 2019). Shahbaz, Naeem, et al. (2018a) also has the similar beliefs that natural resources improve financial development. Yiqing et al. (2017) believe that financial agglomeration promotes the upgrading of industrial structure by pro­ moting regional innovation and it indirectly promote the improvement of ecological efficiency. Yigen and Kaiwen (2018) explored the impact of financial agglomeration and industrial structure on regional eco-efficiency. The results show that both financial agglomeration and industrial structure have a stable and positive impact on regional ecological efficiency. It shows that financial agglomeration and indus­ trial structure optimization contribute to the improvement of regional ecological efficiency, and there are obviously regional differences in the impact of financial agglomeration and industrial structure optimization on ecological efficiency. Furthermore, previous research also focused on the coupling and development between financial agglomeration, innovation and ecolog­ ical efficiency from a system perspective. At present, the coupling of technological innovation and financial innovation in most regions has been significantly improved (Yingying and Renxiang, 2017). However, the coupling of financial agglomeration and eco-efficiency mostly shows

2. Literature review For a long time, scholars have emphasized on conducting in-depth 2

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that the utilization rate of financial resources is not high, and the support for ecological efficiency is also insufficient (Caiquan et al., 2014). In addition, studies are also available on the coupling and coordination between financial agglomeration, industrial structure and ecological efficiency such as the study of Yiqing et al. (2015). From the point of view of the current literature, when discussing the coupling and coor­ dination relationship among variables, most of the existing studies use the coupling theory to construct the coupling and coordination degree model (Qinghua et al., 2019; Yiqing et al., 2015) and then calculate the coupling and coordination degree coefficients among the variables. Ran Wang et al. (2017) measured coupling coordination of resources and environment. Shahbaz et al. (2019) indicated the role of natural re­ sources toward carbon emissions. Senyu and Bo (2017) innovatively applied the theory of synergy, constructed a coupling coordination de­ gree evaluation model of regional innovation and economic system, and measured the coupling coordination degree of 30 provincial-level re­ gions (excluding Hong Kong, Macao and Taiwan, Tibet). Li et al. (2018) measured the coordinated development of economy, natural resources and environment. Shahbaz et al. (2015) measured the role of industrial development toward environmental degradation. Most of the existing literature focuses on exploring the relationship between financial development, innovation or industrial structure and ecological effi­ ciency. In addition, many studies in the past have explored the coupling coordination among financial development, financial agglomeration and ecological efficiency, but no study in the past incorporated the role of natural resources in the coupling coordination model. However, Chenglin and Xueping (2016) believe that as the economic demand continues to grow, the natural resource endowment stimulates the “distributive efforts”. Similarly, it will significantly inhibit the progress of eco-efficiency and have a “ratchet effect” on production efficiency. Therefore, the integration of natural resource endowment into the research framework of financial development and eco-efficiency, exploring the cooperation between financial development, natural re­ sources and ecological efficiency is conducive to make up for the current research gap and increase the systematic nature of research. To measure ecological efficiency, scholars have used a single index method and multi-index method (Wang et al., 2015) factor analysis method (Wu et al., 2012), analytic hierarchy process (Xiaoyan et al., 2019), decoupling analysis method to measure regional ecological effi­ � te_ and Juknys, 2012; Wang et al., 2015). Jingru et al. ciency (Dagiliu (2014) used the analytic hierarchy process to evaluate the ecological efficiency of Zhengzhou Economic and Technological Development Zone. Shenglan et al. (2014) adopted the DEA model to measure the regional ecological efficiency of China, and explored the effect of envi­ ronmental regulation on ecological efficiency. Mingran and Jun (2016) used the DEA-Tobit two step method to measure and analyze the influencing factors of ecological efficiency of 31 provinces in China. These methods have revealed the development of ecological perfor­ mance in different regions from different perspectives. Moreover, these studies have certain reference functions for revealing the relationship between regional economic development, resources and environment, but they only capture certain aspects of ecological efficiency. However, these studies failed to fully and objectively reflect the current situation of regional ecological efficiency, and the subjective arbitrariness of index weight setting is bigger. The study of (Chenglin and Xueping, 2016) adopt factor analysis and principal component analysis which mainly focus on the evaluation of regional ecological performance, especially in the evaluation process without considering resource con­ sumption and its environmental impact. Although the decoupling analysis method reflects the resource and environmental costs of eco­ nomic performance, it is mainly used to investigate the future devel­ opment trend of ecological efficiency. What is needed now is to faithfully depict and accurately describe the past performance and current situation of ecological performance. Dan and Junjie (2016) pioneered the use of GDP per unit of ecological footprint to measure eco-efficiency. The advantage of this method is to comprehensively

consider the output and ecological indicators, to measure and evaluate China’s ecological efficiency (to represent the efficiency of resource utilization), and to decompose the change of ecological efficiency into changes in total factor productivity and changes in factor substitution. Similarly, the reasons for changes in ecological efficiency can be analyzed. However, this method also has some disadvantages, such as this method only considers one type of waste generated by humans, namely carbon dioxide (CO2), it ignores other waste gases, and neglecting other wastes. For example, this method also ignores non-energy minerals such as iron, mineral and copper consumed by human beings. In addition, there are also some controversies about the weights used in the conversion of various types of land. Therefore, new study is pivotal to overcome these issues and explore the coordinated development of natural resources, financial development and ecological efficiency. In addition, there are also issues in measuring financial development. If we look into the existing literature, it can be seen that most of the studies used location entropy for the selection of financial development indicators (Ciccone and Hall, 1993; Linxin et al., 2017; Qinghua et al., 2019). Index or single indicators are used to calculate the financial agglomeration or financial scale of a region, and less consideration is given to the use of the entropy weight method to construct an index system to measure it (Shuguang et al., 2006; Tao et al., 2005). Moreover, the relevant literature usually uses a single indicator to measure natural resource systems, such as the study of Chenglin and Xueping (2016), and it is measured by a less systematic and comprehensive index system. Therefore, it is necessary to adopt a systematic method to comprehen­ sively develop an index system for measuring natural resources. In this study, entropy weight method and coupling coordination degree method used to estimate the coordinated development of natural resources, financial development and ecological efficiency. Entropy weight method has been adopted from information theory. Basically, this method is built upon the Shannon entropy, which was originally proposed by Shannon (1948). This concept is used as the measure of uncertainty in information processing which was articulated in context of probability theory. This method is widely used in recent studies. Such as the study of Xu et al. (2018) used entropy weight method in flood risk assessment. Cui et al. (2018) employed this method to assess the water resources. Delgado et al. (2019) used entropy weight method to assess the social impact on mining projects. Therefore, it can be stated that this method has capability to be used in various context where information processing is being used. L. L. Shen et al. (2018) highlighted that in recent years, the coupling coordination method is being used in envi­ ronmental studies. Similarly, as our study also focused on ecological efficiency. Therefore, we complemented entropy weight method with coupling coordination degree evaluation method. Coupling is an aca­ demic term in physics. It means that two or more systems interact with each other through coordination (Q. Song et al., 2018). The study of N. Liu et al. (2018) employed coupling coordination method to estimate the coordination degree between urbanization and environment. Sun et al. (2018) used this method to assess vulnerability. Although, this method is being widely used in environmental context, but none of the study used this method to determine the coordination degree between natural re­ sources, financial development and ecological efficiency. Therefore, we believe our study is pioneer in this context. 3. Materials and methods 3.1. Entropy weight method In information theory, entropy is a measure of uncertainty (Q. A. Wang, 2008). In other words, it can be stated that the high level of in­ formation, the low level of uncertainty and the lesser the entropy. Whereas, the lower level of information, the higher the uncertainty and the bigger the entropy. Based upon the characteristics of entropy, one can evaluate a scheme by calculating the entropy value. The degree of 3

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randomness and disorder can also be used to estimate and understand the degree of dispersion of an index. Similarly, the higher level of dispersion of an indicator means that certain indicator greater impact on the comprehensive evaluation. In the light of information theory and its principles, information is a determinant factor to measure the order degree of certain system. In contrast, entropy is used to measure and explore a disorder in the system. Similarly, entropy is regarded as a degree of the disorder in a certain system. In a situation, when infor­ mation entropy of a specific indicator is lower, it means information provided by this indicator are higher, which is basically in the system of comprehensive evaluation. Similarly, when the role is bigger, then the weight should be higher. Therefore, the information entropy method can be employed to estimate the weight of each indicator, which provides a basis for comprehensive evaluation of multiple indicators. The assess­ ment process is shown in equation (1): 0 1 X11 ⋯ X1m @ A¼ ⋮ ⋮ ⋮ A (1) Xn1 ⋯ Xnm n�m

The six step is to calculate the comprehensive score of each program: m X

Si ¼

Coupling is an academic term in physics. It means that two or more systems interact with each other through coordination (Q. Song et al., 2018). The degree of coupling and coordination reflects the degree of harmony between development and evolution to reveal the disorder from uncoordinated to orderly coordination of the development trend. Because of the interrelationship between financial development, natural resources and ecological efficiency, it can be presumed that they can influence each other. Therefore, the coupling coordination theory can be applied to the study the interaction between financial development, natural resources and ecological efficiency. Based on the volume coupling coefficient model in physics, a multi-system coupling degree model is constructed. The model is shown in equation (8): rffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi. ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi Y n Cn ¼ n ðu1 � u2 � ⋯ � un Þ (8) ui þ uj Based on the model shown in equation (8), we construct a coupling model of financial development - natural resources - eco-efficiency ternary system, the model is shown in equation (9): pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 3 3 U1 � U2 � U3 C¼ (9) U1 þ U2 þ U3 where (equation (9)): C represents the degree of coupling; U1, U2, U3 represent the comprehensive evaluation index of three systems of financial development, natural resources and ecological efficiency. Since the coupling degree model only shows the strength of the inter­ action and cannot reflect the level of coordinated development, there­ fore, based on the relevant research, the coupling coordination model of the three systems is constructed in equation (10): pffiffiffiffiffiffiffiffiffiffiffiffi D ¼ C � T ; T ¼ αU1 þ βU2 þ γU3 (10)

(2)

where (equation (2)) Xij is the value of the jth indicator of the ith scheme. For the indicator of small and better the indicator: � Xij max X1j ; X2j ; ⋯; Xnj � �þ1 X ’ij ¼ ;i min X1j ; X2j ; ⋯; Xnj max X1j ; X2j ; ⋯; Xnj ¼ 1; 2; ⋯; n; j ¼ 1; 2; ⋯; m

where (equation (10)): D is the coupling coordination degree; C is the coupling degree; T is the comprehensive evaluation index of the research area financial development - natural resources - ecological efficiency; α, β, γ represent the financial development, natural resources and ecolog­ ical efficiency system in the whole weight system. When determining the weight, a combination of expert scoring and comprehensive investiga­ tion is adopted. Comprehensive measurement, finally determined α ¼ 0. 25, β ¼ 0. 40, γ ¼ 0. 35. In order to make a more accurate judgment of the degree of coupling coordination between the three systems in the study area, the coupling coordination degree is classified by the uniform distribution function method (Table 1).

(3)

For the sake of convenience, the data after non-negative processing is still recorded as Xij . The second step is to calculate the proportion of the ith plan under the jth indicator. Xij Pij ¼ P n Xij

ðj ¼ 1; 2; ⋯mÞ

(7)

ði ¼ 1; 2; ⋯nÞ

3.2. Coupling coordination degree evaluation method

In this paper, the subsequent procedure is used following the studies of Junhan and Mingxiu (2018) and Lei et al. (2017). The first step is non-negative processing of data. Since the entropy method calculates the ratio of a certain indicator of each scheme to the sum of the same index value, there is no dimension effect, and no standardization is needed. If there is a negative number in the data, the data needs to be non-negative. In addition, in order to avoid the meaninglessness of the logarithm when entropy is sought, data translation is required: For the indicator of bigger and better the indicator: � Xij min X1j ; X2j ; ⋯; Xnj � �þ1 X ’ij ¼ ;i max X1j ; X2j ; ⋯; Xnj min X1j ; X2j ; ⋯; Xnj ¼ 1; 2; ⋯; n; j ¼ 1; 2; ⋯; m

Wj *Pij

j¼1

(4)

i¼1

The third step is to calculate the entropy of the jth indicator: n X

ej ¼

k*

Pij log Pij

�

;

(5)

i¼1

1 where (equation (5)) k > 0ej � 0, k ¼ lnm ,0 � e � 1 The fourth step is to calculate the coefficient of variation of the jth indicator: For the jth indicator, the greater the difference in the index value Xij , the greater the effect on the evaluation of the scheme, the smaller the entropy value. gj ¼ 1 ej , Then: the bigger the gj , the more important the indicator. The fifth step is to solve the weight:

gj Wj ¼ P m gj

; j ¼ 1; 2⋯m

Table 1 Classification of coupling degree and coupling coordination degree. C value interval

Coupling type

D value interval

Coupling coordination type

0＜C�0.3

Low level coupling Antagonistic phase Run-in phase

0＜D�0.3

Low coupling coordination

0.3＜D�0.5

High level coupling

0.8＜D＜1

Moderate coupling coordination Highly coupled coordination Extreme coupling coordination

0.3＜C�0.5 0.5＜C�0.8

(6)

0.8＜C＜1

j¼1

4

0.5＜D�0.8

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4. Results and findings

Table 2 Indicator evaluation system.

4.1. Index system construction In order to ensure effective measurement, this paper divides the financial development system into three first-level indicators and 11 slevel indicators. The study divides the eco-efficiency system into four first level indicators and thirteen secondary indicators. The natural resource system divided into two first-level indicators and the 8 s-level indicators. The first level and second level indicators for financial development, natural resources and ecological efficiency are shown in Table 2. All the data has been taken from the 2006–2018 China Statis­ tical Yearbook, China City Statistical Yearbook, China Environmental Statistics Yearbook, China Financial Statistics Yearbook and the statis­ tical yearbooks of the provinces in China.

System

subsystem

evaluation index

nature

weight

Financial development system

Financial scale

Total deposits of financial institutions (100 million yuan) Total loans of financial institutions (100 million yuan) Household savings (100 million yuan) Number of financial employees (a) Number of legal entities in the financial industry (units) Deposit ratio (%) Insurance density (%) Insurance depth (%) Financial correlation rate (%) Fixed assets investment (100 million yuan) Total social retail sales (100 million yuan) Waste water discharge (tons) Exhaust emissions (100 million cubic meters) Solid waste discharge (tones) Energy consumption rate (%) Water production rate (%) Construction land output rate (%) Per capita GDP (yuan) Gross industrial output value (ten thousand yuan) Fixed assets investment in the whole society (100 million yuan) Comprehensive utilization rate of solid waste (%) Sewage treatment rate (%) Garbage harmless treatment rate (%) Forest cover rate (%) Total water resources (10,000 tons) Total land resources (thousand acres) Total energy (ten thousand tons) Per capita cultivated area (m2/person) Per capita power generation (kw.h/ person) Per capita living water consumption (L/ person) Per capita raw coal production (kg/ person) Per capita crude oil consumption (kg/ person)

þ

0.1332

þ

0.0654

þ

0.0735

þ

0.0199

þ

0.0323

þ þ þ þ

0.1012 0.1544 0.1241 0.0912

þ

0.1123

þ

0.0921

–

0.1231

–

0.1121

–

0.1681

þ

0.0293

þ

0.0223

þ

0.0612

þ þ

0.0762 0.0852

þ

0.0431

þ

0.0686

þ

0.0861

þ

0.0981

þ þ

0.0266 0.2113

þ

0.1151

þ

0.1465

þ

0.1412

þ

0.1503

þ

0.0921

þ

0.0821

þ

0.0614

Financial efficiency

4.2. Rational of variables included in an index 4.2.1. Variables selection for financial development The subsystem index system is composed of multiple indicators. Due to the complexity of the financial development system itself, con­ structing a financial development subsystem index which should be a multi-dimensional and comprehensive and include reasonable in­ dicators. At present, experts and scholars have an index system for the financial development system. Luo et al. (2013) refined the financial agglomeration system into three primary indicators including financial scale, financial efficiency and financial environment to measure the degree of financial agglomeration in East China. Y. Q. He et al. (2015) emphasized upon the scale of overall financial industry, the securities industry, and the insurance industry and described the status of financial agglomeration in the Yangtze River Economic Belt. F. Liu et al. (2017) measured the degree of financial agglomeration in Hunan Province from a macro perspective from the financial aggregate and financial quality. Here, financial quality reflects the efficiency of financial resource allo­ cation in the region. J. H. Wang and Ma, 2018 takes into account the factors such as population size and selects highly comparable per capita indicators to reflect the degree of regional financial agglomeration. In terms of per capita financial industry value-added, per capita premium income, and financial industry employees, indicators were selected to measure the degree of financial agglomeration in various cities in Hei­ longjiang Province. In the construction of China’s financial development indicator sys­ tem, it is necessary to give full consideration to the actual situation of China as a whole and various regions. Therefore, it is necessary to emphasize on existing experts and scholars’ selection methods and classification standards to objectively reflect the overall situation of China’s financial development. Similarly, this article starts from three aspects of financial scale, financial efficiency and financial environment, and selects 11 indicators including the total deposits and loans of financial institutions, financial-related rates, and fixed asset investment. Among them, the financial scale indicator can directly reflect the overall situation of financial development in various regions of China. Based on the representative principles selected by the indicator and the existing data, the indicator is finally determined as the total deposits of financial institutions, total loans of financial institutions, and household savings. Further, the number of employees in the financial industry and the number of legal entities in the financial industry and the financial effi­ ciency indicators are used to reflect the input-output relationship of the financial industry. The financial correlation rate is used as a secondary indicator. The financial environment indicator reflects the overall socioeconomic environment of China’s financial development. In addition, this study selects the amount of fixed asset investment and total social retail sales to measure the financial environment in China and various regions.

Financial environment

Ecological efficiency system

Environmental pollution

Resource Consume

Economic benefit

Circular Economy

Natural resource system

Resource size

Resource efficiency

5

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Resources Policy 65 (2020) 101580

4.2.2. Variables selection for ecological efficiency subsystem The level of regional ecological efficiency is affected by factors such as geographical location and spatial-temporal differences. It is an indi­ cator that interacts and affects multiple factors. In the study of (Y. Q. He et al., 2015) the construction of the index system of the ecological ef­ ficiency subsystem, the environmental efficiency, resource and energy consumption, and circular economy were used to evaluate the ecological efficiency of the Yangtze River Economic Belt. Further, eleven indicators including waste water, waste gas and smoke and dust emissions and output rates, energy and water consumption output rates, and forest coverage were selected. Caiquan et al. (2014) measured the ecological efficiency of China’s provinces from four aspects: ecological benefits, resource consumption, economic benefits, and circular economy. Cao and Li (2018) selected indicators such as fixed capital input and total employment to measure resource consumption. They used three wastes and chemical oxygen demand emissions as indicators to measure envi­ ronmental pollution. Finally, used regional GDP to measure regional economic benefits. When constructing the index system of China’s ecological efficiency subsystem, we should comprehensively consider various aspects, not only follow basic principles such as scientifically and operability. But, also combine the financial development and ecological environmental protection status of China and various regions to make the constructed indicators. Similarly, such index system has practical significance. Therefore, this article selects the corresponding indicators from the four dimensions of environmental pollution, resource consumption, economic benefits and a circular economy to reflect the true level of ecological efficiency in China and various regions. Among them, envi­ ronmental pollution indicators are mainly used to measure the envi­ ronmental pollution situation in China and various regions. The study measures from the three aspects of wastewater discharge, waste gas discharge and solid waste discharge. These three indicators are negative indicators. The lower the indicator, the better the ecological and envi­ ronmental benefits of the area. The resource consumption indicators mainly reflect the resource and energy consumption status of China and various regions. This article mainly selects three indicators: energy consumption output rate, water output rate and construction land output rate. The economic benefit index is mainly used to measure China and the economic output of each region takes into account not only China’s overall economic benefits, but also the individual perspective of each region. This article mainly selects GDP, total industrial output value, and investment in fixed assets of the whole society to measure the economic benefits of China and various regions. Circular economy in­ dicators are mainly used to reflect whether China’s regional economic development meets the requirements of circular economy and sustain­ able development. Circular economy indicators not only require eco­ nomic growth, but also focus on environmental protection and resource recycling. They are indicators that reflect the nature of ecological effi­ ciency. Therefore, based on the actual situation in China and the availability of data, this article selects the comprehensive utilization rate of industrial solid waste, sewage treatment rate, harmless treatment rate of waste, and forest coverage rate to represent the sustainable devel­ opment of China’s economy.

resource efficiency. 4.3. Subsystem comprehensive evaluation analysis Following the establishment of evaluation index, the study calculate coupling degree and coupling coordination degree of east, central and western region of China. According to the established evaluation index system presented in Table 2, based on the coupling degree and coupling coordination degree model, the coupling degree and coupling coordi­ nation degree of China’s three major regions in 2006-2018 have been calculated. The summarized results are presented in Table 3, Table 4 and Table 5. 4.3.1. Financial development system In order to reflect the trend of financial development, natural re­ sources and ecological efficiency more intuitively, this paper draws a line chart to comparison and show the changes in financial development systems in three regions based on Table 3, Table 4 and Table 5. Fig. 1 shows the trend of financial development in the three major regions. It can be seen that the overall trend of financial development in the three regions is improved. However, during both years i.e. 2008 and 2012 financial development declined in the eastern, central and western re­ gions. These two points are inseparable from the changes in the domestic economic environment. In 2008, the Chinese economy was affected by the international financial crisis. It can be seen from the chart that the domestic economy was hit hard by the financial crises of 2008. More­ over, during year 2012, the finance sector of the country faced more severe challenges. The financial industry’s bad debts increased, entre­ preneurs left their businesses and corporate liquidation went bankrupt. The phenomenon was very serious and it has significantly influenced economic landscape in the country. To address this crisis, the govern­ ment actively adopted policies to promote the healthy development of the financial industry. In particular, the country proposed the Belt and Road Initiative in 2013, which gave significant support to financial development. Since then, financial development has achieved remark­ able development in three regions, and in 2014–2018, the growth rate of financial development of the central and western regions were higher than that of the east, mainly because the role of financial development in the central and western regions was increasing. However, the strength of financial development in the eastern region is still obvious, mainly due to the relatively developed economy in the eastern region and the high level of financial innovation. Therefore, financial development has a greater role in promoting the eastern region. 4.3.2. Natural resource system According to Table 3, Table 4 and Table 5, it can be seen from Fig. 2 that during the time period under evaluation of this study, the fluctua­ tions of the natural resources system are relatively stable and the overall trend is increasing. It can be seen that before 2010, the natural resource endowment of the western region was the highest and the lowest was in the east. This was mainly due to the lack of resources in the early eastern region, the contribution to the economy was smaller than that in the central and western regions, and the central and western regions had abundant natural resources. The advantages obtained from natural re­ sources are higher than those in the east, but after 2010, especially the 4 trillion investment proposed by the state in 2009 has a greater effect on economic growth. Although the eastern region has lack of resources, but its efficiency of resource utilization is high. The central and western regions of the country are rich in resources, but the utilization efficiency is low. Therefore, it is hereby highlighted that the eastern part of the country surpasses the central and western regions. Moreover, it can be seen from the figure that the growth of the central region is closer to that of the east, while the growth of the west is slower. It further demon­ strates that the regions with relatively backward economic development are over-reliant on resources. Most of the natural resources industries are located upstream of the industrial chain. Therefore, the low level of

4.2.3. Variables selection for natural resources subsystem The natural resource index uses energy, water, and land resources. These resources are important material resources and essential for regional economic development, production and living. If resources are scarce, it will also restrict the economic development of the region to some extent. It mainly reflects the problem of coordinated development of resources from the total amount and efficiency. This article selects the total amount of water resources, total land resources and total energy from the total resources to measure the scale of resources. In terms of resource efficiency, the per capita arable land area, per capita power generation, per capita domestic water consumption, per capita raw coal production, and per capita crude oil consumption are used to measure 6

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Table 3 Coordination degree values and grades in the eastern region. Year

Financial development U1

Natural resources U2

Ecological efficiency U3

Coupling degree C

Coupling coordination degree D

Coupling level

Coordination level

Coupling coordination feature

2006

0.1233

0.1334

0.3854

0.8647

0.4352

2007

0.1235

0.1339

0.3951

0.8595

0.4375

2008

0.1009

0.1373

0.3432

0.8677

0.4168

2009

0.1213

0.0912

0.3209

0.8580

0.3920

2010

0.2176

0.1732

0.3987

0.9377

0.4968

2011

0.2619

0.1844

0.4443

0.9360

0.5253

2012

0.2159

0.2455

0.5188

0.9238

0.5553

2013

0.3956

0.2951

0.6523

0.9468

0.6493

2014

0.5125

0.3787

0.6631

0.9745

0.7061

2015

0.5487

0.4252

0.6818

0.9818

0.7321

2016

0.5748

0.4616

0.6888

0.9868

0.7496

2017

0.6089

0.5196

0.7023

0.9925

0.7754

2018

0.6312

0.5952

0.7088

0.9973

0.8014

Mean

0.3412

0.2903

0.5310

0.9329

0.5902

High level coupling High level coupling High level coupling High level coupling High level coupling High level coupling High level coupling High level coupling High level coupling High level coupling High level coupling High level coupling High level coupling High level coupling

Moderate coupling coordination Moderate coupling coordination Moderate coupling coordination Moderate coupling coordination Moderate coupling coordination Highly coupled coordination Highly coupled coordination Highly coupled coordination Highly coupled coordination Highly coupled coordination Highly coupled coordination Highly coupled coordination Highly coupled coordination Highly coupled coordination

Financial development relatively lagging Financial development relatively lagging Financial development relatively lagging Natural resource relative lagging Natural resource relative lagging Natural resource relative lagging Natural resource relative lagging Natural resource relative lagging Natural resource relative lagging Natural resource relative lagging Natural resource relative lagging Natural resource relative lagging Natural resource relative lagging Natural resource relative lagging

Table 4 Central region coordination degrees and ratings. Year

Financial development U1

Natural resources U2

Ecological efficiency U3

Coupling degree C

Coupling coordination degree D

Coupling level

Coordination level

Coupling coordination feature

2006

0.0533

0.1438

0.1054

0.9236

0.3155

2007

0.0635

0.1449

0.1289

0.9415

0.3346

2008

0.0309

0.1483

0.1162

0.8232

0.2978

2009

0.0536

0.1122

0.1076

0.9491

0.3018

2010

0.0953

0.1832

0.1976

0.9521

0.3979

2011

0.1245

0.1945

0.2032

0.9771

0.4194

2012

0.1134

0.2155

0.2055

0.9613

0.4234

2013

0.1956

0.2451

0.2133

0.9957

0.4697

2014

0.2134

0.2822

0.2587

0.9933

0.5050

2015

0.2457

0.3552

0.2834

0.9884

0.5470

2016

0.2758

0.4156

0.3133

0.9851

0.5829

2017

0.3342

0.4695

0.3429

0.9877

0.6217

2018

0.3843

0.5256

0.4173

0.9911

0.6696

Mean

0.1680

0.2643

0.2226

0.9592

0.4528

High level coupling High level coupling High level coupling High level coupling High level coupling High level coupling High level coupling High level coupling High level coupling High level coupling High level coupling High level coupling High level coupling High level coupling

Moderate coupling coordination Moderate coupling coordination Low coupling coordination Moderate coupling coordination Moderate coupling coordination Moderate coupling coordination Moderate coupling coordination Moderate coupling coordination Highly coupled coordination Highly coupled coordination Highly coupled coordination Highly coupled coordination Highly coupled coordination Moderate coupling coordination

Financial development relatively lagging Financial development relatively lagging Financial development relatively lagging Financial development relatively lagging Financial development relatively lagging Financial development relatively lagging Financial development relatively lagging Financial development relatively lagging Financial development relatively lagging Financial development relatively lagging Financial development relatively lagging Financial development relatively lagging Financial development relatively lagging Financial development relatively lagging

development of the resource-related industries has a very limited ability to drive the development of other industries, resulting in inefficient use of natural resources in the region.

4.3.3. Ecological efficiency system Following the mechanism of financial development and natural re­ sources systems, we also draw a line chart (Fig. 3) to express ecological efficiency system. It can be seen from Fig. 3 that the ecological efficiency level in the eastern region is generally higher, and the average of the 7

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Resources Policy 65 (2020) 101580

Table 5 Western region coordination degrees and ratings. Year

Financial development U1

Natural resources U2

Ecological efficiency U3

Coupling degree C

Coupling coordination degree D

Coupling level

Coordination level

Coupling coordination feature

2006

0.0423

0.2334

0.0242

0.6207

0.2641

2007

0.0438

0.2337

0.0249

0.6290

0.2668

2008

0.0409

0.1765

0.0233

0.6880

0.2474

2009

0.0436

0.1812

0.0234

0.6886

0.2511

2010

0.0658

0.1732

0.0411

0.8318

0.2886

2011

0.0665

0.1837

0.0425

0.8238

0.2941

2012

0.0633

0.1843

0.0467

0.8326

0.2969

2013

0.0952

0.2156

0.0497

0.8377

0.3267

2014

0.1234

0.2486

0.0658

0.8660

0.3644

2015

0.1358

0.2673

0.0923

0.9061

0.3961

2016

0.1655

0.3012

0.1143

0.9222

0.4315

2017

0.2312

0.3267

0.1365

0.9403

0.4713

2018

0.2741

0.3412

0.1608

0.9541

0.4993

Mean

0.1070

0.2359

0.0650

0.8108

0.3383

High level coupling High level coupling High level coupling High level coupling High level coupling High level coupling High level coupling High level coupling High level coupling High level coupling High level coupling High level coupling High level coupling High level coupling

Low coupling coordination Low coupling coordination Low coupling coordination Low coupling coordination Low coupling coordination Low coupling coordination Low coupling coordination Moderate coupling coordination Moderate coupling coordination Moderate coupling coordination Moderate coupling coordination Moderate coupling coordination Moderate coupling coordination Moderate coupling coordination

Ecological efficiency relatively lagging Ecological efficiency relatively lagging Ecological efficiency relatively lagging Ecological efficiency relatively lagging Ecological efficiency relatively lagging Ecological efficiency relatively lagging Ecological efficiency relatively lagging Ecological efficiency relatively lagging Ecological efficiency relatively lagging Ecological efficiency relatively lagging Ecological efficiency relatively lagging Ecological efficiency relatively lagging Ecological efficiency relatively lagging Ecological efficiency relatively lagging

Fig. 1. Comparison of changes in financial development systems in three regions.

statistical years (2006–2018) found 0.5310. If we look at the results, it is seen that the value of ecological efficiency score declined during 20082009, mainly due to the outbreak of the US financial crisis in 2008 and the impact of China’s response to the financial crisis. Strategically, 4 trillion investments were proposed at that time. Sudden high-level in­ vestment had a certain negative impact on the environment which is being highlighted in this study. Therefore, it fell back in 2009, which is consistent with the facts of China. This is the same situation in the central and western regions. The ecological efficiency in the central part

is lower than that in the east, but it is larger than that in the western region. However, the difference is that the ecological efficiency changes in the central and western regions are significantly higher than those in the east after 2013. Since 2013, the growth rate of ecological efficiency in the central and western regions is significantly higher than that in the eastern region. Although the eastern region has experienced steady growth, but the growth rate in the central and western regions has comparatively increased. With summing up the discussion, it can be stated that the main reason behind ecological efficiency changes is that 8

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Resources Policy 65 (2020) 101580

Fig. 2. Comparison of changes in natural resources systems in the three major regions.

Fig. 3. Comparison of three regional ecological efficiency systems.

China proposed the “One Belt One Road” construction strategy in 2013. The implementation of the “One Belt One Road” strategy has brought great development opportunities to the central and western China, and there have been major changes in the way of economic development. The external demand for environmental protection has promoted the upgrading of the central and western industries, and the ecological environment has been significantly improved, resulting in a significant improvement in ecological efficiency.

since year 2006-2010, the coupling degree and coupling coordination degree increased to different levels, the coupling degree increased from 0.8647 to 0.9377, and the coupling coordination degree increased from 0.4352 to 0.4968. It indicates that the differences between the systems in the eastern region are gradually narrowing and the development status and speed are converging. From 2011 to 2018, the degree of coupling development is relatively stable, fluctuating between 0.9360 and 0.9973, while the coupling coordination degree is slowly rising from 0.5253 to 0.8014. At this stage, the development of each system is relatively close, and the development level is getting better, indicating that the coupling of the subsystems in the eastern region is in good condition. From the perspective of coupling coordination, the coupling coordination from 2006 to 2010 is in the stage of moderate coupling and coordination, but the financial development in this stage is lagging

4.4. Coupling degree and coupling coordination analysis 4.4.1. Analysis of coupling degree and coupling coordination degree in the eastern region From the trend of coupling degree and coupling coordination degree 9

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Resources Policy 65 (2020) 101580

behind. The backward development of the financial industry limits the coupling and coordination, mainly the development level of the finan­ cial industry in the early stage of development in the region. From 2011 to 2017, the eastern coupling coordination degree entered a state of highly coupled and coordinated development. Since 2011, the level of financial development was relatively lagging, and the rest of the years natural resources were lagging behind the financial development. Dur­ ing the “Twelfth Five-Year Plan” period, the investment in fixed assets in the eastern region was strong, which effectively promoted economic development and improved the level of local financial development. However, the natural resources in the eastern region were relatively limited and could not keep up with the pace of economic development, resulting in a relative lag in the development of natural resources. In 2018, the degree of coupling coordination reached an extreme degree of coordination, indicating that the eastern region’s economy is highly developed and promoted the coordinated development of the natural resources, financial development and ecological efficiency.

Five-Year Plan” period, the construction of the Belt and Road Initiative has driven the rapid development of the financial industry in the western region, but most of the funds were flowing to the secondary industry, a large number of industrial emissions and construction frequently cause serious air pollution, resulting in low levels of ecological efficiency. The overall eco-efficiency of the western region appears to be lagging behind. It can be seen that eco-efficiency is an important factor restricting the coupling degree and coupling coordination of the western region. The western region should vigorously develop clean energy and develop green industries, and take into account environmental protec­ tion while developing the economy, thereby improving various sub­ systems of the coordinated development. 5. Discussion and conclusion In recent years, many scholars have emphasized on the measuring ecological efficiency by using different methods. Moreover, they explored the relationship between financial development and ecological efficiency. Also, the impact of natural resources on eco-efficiency, but no study includes financial development, natural resources and ecosystems in the same system and studies the interaction between the three from a systemic perspective. This would lead a shortcoming in the previous research. Although, different studies used distinguished methods for the analysis and developed indexes, but there are still some controversies about the weights used in the conversion of various types of land. Therefore, new study was pivotal to overcome these issues and explore the coordinated development of natural resources, financial develop­ ment and ecological efficiency. In this study, we integrate financial development, natural resources and ecological efficiency into the same system to consider the coordinated development between them. Through the systematic and coordinated development between natural resources, financial development and ecological efficiency, the devel­ opment model of financial development-natural resources and ecolog­ ical efficiency has been constructed which provides a useful reference for the systematic and coordinated development of natural resources, financial development and ecological efficiency in context of three re­ gions of China. The results from the study has shown that the overall trend of financial development in the three regions is improved during the time period under analysis. However, during both years i.e. 2008 and 2012, the financial development declined in the eastern, central and western regions. The study provides evidence of the impact of 2008 financial crises on Chinese economy. Our results are in line with the studies of Overholt (2010) and N. N. Shen et al. (2018) which highlights that global financial crises of 2008 badly affected Chinese economy. Kwong (2019) also have similar beliefs that financial crises and trade ward significantly influenced the Chinese economy. During this time exports fall due to which financial development in the country was slow down. This situation continues and the phenomenon become more serious in year 2012 and it significantly changed the economic landscape in the country. As the financial industry’s bad debts increased, entrepreneurs left their businesses and corporate liquidation went bankrupt during this time. This crisis awakened the policy maker’s attention and the gov­ ernment actively adopted policies to promote the healthy development of the financial industry. Particularly, the Belt and Road Initiative, which gave significant support to financial development in the country. Saud et al. (2019) explored the nexus between financial development, income level and environment in context of belt and road initiatives. This is also evident from the results of our study that right after the initiative of “Belt and Road” financial development has achieved remarkable development in three regions. Furthermore, it was found that the fluctuations of the natural re­ sources system are relatively stable and the overall trend is increasing. It can be seen that the natural resource endowment of the western region was the highest and the lowest was in the east in the early years of evaluation. This was mainly due to the lack of resources in the early

4.4.2. Analysis of coupling degree and coupling coordination degree in the central region The coupling degree and coupling coordination degree of the central region are shown in Table 4. The coupling degree of the natural re­ sources, financial development and ecological efficiency increased from 0.9236 to 0.9957 in 2006-2013, and the coupling coordination degree increased from 0.3155 to 0.4697. It is in a steady upward trend with a high degree of coupling. It shows that there is a close relationship be­ tween the natural resources, financial development and ecological effi­ ciency. The coupling degree increased from 0.5050 to 0.6696 in 20142018, which is also on the rise. It can be seen that the coupling coor­ dination degree in the central region is lower than that in the eastern region. From the perspective of the degree of coupling and coupling coordination, the trends of coupling degree and coupling coordination degree are similar from 2006 to 2018, which can be divided into two stages: the first stage is from 2006 to 2014 excluding year 2008, during which the regional coupling coordination of the central part is moder­ ately coordinated. In 2008, the financial crisis has had a major negative impact on regional economic development. The financial industry has suffered more serious damage, thus reducing coupling coordination. Research on its internal financial development is relatively lagging behind. Secondly, from year 2005–2008, the rate of increase was rela­ tively fast, and it entered the stage of moderate coupling and coordi­ nation, but financial development still lags behind development. 4.4.3. Analysis of coupling degree and coupling coordination degree in western region The coupling degree and coupling coordination degree of the central region are shown in Table 5. The coupling degree of the natural re­ sources, financial development and ecological efficiency increased from 0.6207 to 0.8326 in 2006-2012. The coupling coordination degree increased from 0.2641 to 0.2969 during the same period. Steady upward trend, but the overall coupling level is low, and the subsystem devel­ opment status is at a low level. In 2013–2018, the coupling coordination degree of the three is greater than 0.3 but less than 0.5, which is still at a low level. The types and trends of coupling degree and coupling coor­ dination degree from year 2006–2018 are similar, both are due to the upward trend and can be divided into two stages: the first stage is from 2006 to 2012, during which the coupling and the coordination level of western region is lower than 0.3. Therefore, it is categorized as a low coupling phase. Whereas, during the era of 2013-2018, the value of coupling cooperation degree is greater than 0.3 but less than 0.5. Thus, it shows that coupling level enters into the moderate coupling phase. It can be found that the coupling coordination degree in the western region is much lower than that in the eastern region and also lower than the central region. At the same time, the ecological efficiency subsystem of the western region is significantly lower than that of the eastern and central regions. During the “Twelfth Five-Year Plan” and the “Thirteenth 10

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Resources Policy 65 (2020) 101580

eastern region. C. He et al. (2014) talked about the lack of resources and its consequences for environmental management. Bekun et al. (2019) have also emphasized on resources and environmental issues. Although, central and western regions had abundant natural resources and the advantages obtained from natural resources were higher in the past, but the 4 trillion investment proposed by the state in 2009 has a greater effect on economic growth of all regions. Zheng et al. (2013) highlighted the importance of the aforementioned 4 trillion investment in their study. They found that 4 trillion investment invest exhibits a highly persistent effect on Chinese economy. Moreover, we found that although the eastern region has lack of resources, but its efficiency of resources utilization is high. The central and western regions of the country are rich in resources, but the utilization efficiency is low. Therefore, it is hereby highlighted that the eastern part of the country surpasses the central and western regions. It further demonstrates that the regions with relatively backward economic development are over-reliant on resources. Most of the natural resources industries are located upstream of the industrial chain. Therefore, low level of development of the resource-related industries has a very limited ability to drive the development of other industries, resulting in inefficient use of natural resources in the region. On the one end, it is found that 4 trillion investment proposed by the state in 2009 has a greater effect on economic growth. On the other end, negative consequences of this investment has been highlighted. We found that due to outbreak of US financial crises in 2008 Chinese economy was significantly influenced. Therefore, to tackle these effects policy makers of China proposed 4 trillion investment at that time. Although, the huge investment brought many economic opportunities, but sudden high-level investment had a certain negative impact on the environment which has been highlighted from the results of this study. The ecological efficiency in the central part is lower than that in the east, but it is larger than that in the western region. Since 2013, the growth rate of ecological efficiency in the central and western regions is significantly higher than that in the eastern region. Although, the eastern region has experienced steady growth, but the growth rate in the central and western regions have comparatively increased. In conclu­ sion, it can be stated that the main reason behind ecological efficiency changes is that China proposed the “One Belt One Road” construction strategy in 2013. The implementation of the “One Belt One Road” strategy has brought great development opportunities to the central and western China, and there have been major changes in the way of eco­ nomic development. The external demand for environmental protection has promoted the upgrading of the central and western industries, and the ecological environment has been significantly improved, resulting in a significant improvement in ecological efficiency. Moreover, the coupling degree and coupling coordination degree in the context of eastern region increased to different levels. It is basically the evidence that the differences between the systems in the eastern region are gradually narrowing and the development status and speed are converging. The same trend has been found in the context of central and western regions. Overall, it is in a steady upward trend with a high degree of coupling. It shows that there is a close relationship between the natural resources, financial development and ecological efficiency. However, the financial crisis of 2008 has had a major negative impact on regional economic development. The financial industry has suffered more serious damage, thus reducing coupling coordination in year 2008. During the “Twelfth Five-Year Plan” period, the investment in fixed assets in the eastern region was strong, which effectively promoted economic development and improved the level of local financial development. However, the natural resources in the eastern region were relatively limited and could not keep up with the pace of economic development, resulting in a relative lag in the development of natural resources. In 2018, the degree of coupling coordination reached an extreme degree of coordination, indicating that the eastern region’s economy is highly developed and promoted the coordinated develop­ ment of the natural resources, financial development and ecological

efficiency. Moreover, during the “Twelfth Five-Year Plan” and the “Thirteenth Five-Year Plan” period, the construction of the Belt and Road Initiative has driven the rapid development of the financial in­ dustry in the western region, but most of the funds were flowing to the secondary industry, a large number of industrial emissions and con­ struction frequently cause serious air pollution, resulting in low levels of ecological efficiency. The overall eco-efficiency of the western region appears to be lagging behind. It can be seen that eco-efficiency is an important factor restricting the coupling degree and coupling coordi­ nation of the western region. The western region should vigorously develop clean energy and develop green industries, and take into ac­ count environmental protection while developing the economy, thereby improving various subsystems of the coordinated development. 6. Policy implications Transforming industrial structure and promoting green development. The industrial structure is an important factor affecting ecological efficiency. Similarly, the industrial structure dominated by the primary and sec­ ondary industries account for a large proportion of the industrial structure, has greater environmental pollution and low ecological effi­ ciency. Whereas, the industrial structure dominated by the tertiary in­ dustry account for a small proportion of the industrial structure, has a greater effect on improving ecological efficiency. Moreover, the layout of the industrial structure and a better allocation of resources have positive and negative impacts on ecological efficiency. Whereas, pro­ moting the optimization of an industrial structure will certainly help and guide the green development of the economy. First of all, in the opti­ mization of industrial layout, the government should formulate policies to limit the development of industrial enterprises with high energy consumption and high pollution, guide them in technological upgrada­ tion to reduce resource consumption and environmental pollution, or guide their transformation and development. Secondly, to formulate industrial policies that conform to the development status of various regions, the government need to comprehensively consider the eco­ nomic development status of each region, make overall planning and reasonable arrangements in advance. Third, in terms of government policy support, subsidies and tax incentives should be given to green industries, such as tourism, high-tech industries and service industries. For industries with high energy consumption, high pollution and low efficiency, pollution punishment measures should be increased to gradually promote the formation of a sustainable and recyclable new industrial economic system. Fourth, in terms of the layout of the eco industrial chain, the government should plan the eco system of each region from a macro perspective, upgrade the industrial hardware and software environment, and extend the eco industrial chain. In terms of the specific ecological construction of each region, the government should fully consider the feasibility of technology and the friendliness of the environment, reasonably plan the industrial cluster and gradually build a systematic ecological community. Improve the property right system of natural resource assets and accel­ erate the formation of monitoring mechanism of environmental carrying capacity. At present, the definition of property right of natural resources is not perfect in China, so the pricing mechanism of natural resources is inaccurate. The Third Plenary Session of the 18th Central Committee of the Communist Party of China pointed out that to improve the property right system of natural resource assets and regulate its use. For doing so, the first thing is to make clear the property right of natural resources, form a property right system with clear ownership, clear responsibility and effective supervision and management. Let the market automati­ cally adjust the price of natural resources, so as to reflect the real value of natural resources. In addition, it is vital to explore the multi owner­ ship system of natural resources, allocate natural resources among different stakeholders such as the state, local governments, enterprises and individuals. Further, a pattern of multi ownership subjects can be formed which will accelerate the formation of a monitoring mechanism 11

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for the carrying capacity of resources and environment. It is pivotal to implement restrictive measures for the problem of over limit of water and soil resources and environmental capacity by means of warning prediction and warning degree prediction. Transforming the change development concept and improve ecological awareness. Due to historical reasons, the central and western regions are at a disadvantage in terms of capital accumulation, industrial technol­ ogy and human resources. In the early stage of supporting their rapid economic development of these regions, they can usually only sacrifice the resources and environment. Therefore, in the historical opportunity of advocating the construction of ecological civilization, the central and western regional governments should change the development concept in a timely manner, take the scientific development concept as a guide, transform the economic growth mode, actively develop the circular economy, and integrate the ecological efficiency improvement into the entire regional economic development. Build a financial and resource industry cluster integration platform. Government departments should build a platform for the integration of financial industry and manufacturing enterprises to provide guidance and services for the integration of resource industry clusters and financial industry. On the one hand, the government should actively build a network platform for banks and enterprises to connect, expand and unblock information communication channels between banks and enterprises, and promote the transparency and openness of information. With this platform as a bridge, financial institutions can promote financial products, provide loan services, enterprises can understand credit product information, release financing needs. Further, online financial services are more optimized and convenient. On the other hand, to build a bank enterprise docking platform, the government should regularly organize enterprises, banks and other relevant parties to hold symposiums and other forms of friendship activities, so as to strengthen mutual trust and exchanges between all parties.

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Funding information The authors would like to extend sincere appreciation to the College of Economics and Management, Nanjing University of Aeronautics and Astronautics for providing research funding. This research is supported by the Young Teachers Research Supporting fund Project number (100956SYAH19058). Declaration of competing interest 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. References Ahmed, K., Mahalik, M.K., Shahbaz, M., 2016. Dynamics between economic growth, labor, capital and natural resource abundance in Iran: an application of the combined cointegration approach. Resour. Policy 49, 213–221. Baloch, M.A., Zhang, J., Iqbal, K., Iqbal, Z., 2019. The effect of financial development on ecological footprint in BRI countries: evidence from panel data estimation. Environ. Sci. Pollut. Control Ser. 26 (6), 6199–6208. Bekun, F.V., Alola, A.A., Sarkodie, S.A., 2019. Toward a sustainable environment: nexus between CO2 emissions, resource rent, renewable and nonrenewable energy in 16EU countries. Sci. Total Environ. 657, 1023–1029. Caiquan, B., Bubao, H., Weixuan, S., 2014. Study on the coordinated development of provincial financial agglomeration and ecological efficiency. J. Arid Land Resour. Environ. 28 (9), 1–7. Cao, J.W., Li, X.D., 2018. Measurement and analysis of ecological efficiency in the Yangtze River economic belt. Eco-Economy 34 (8), 174–179. Chen, S., 2015. Environmental pollution emissions, regional productivity growth and ecological economic development in China. China Econ. Rev. 35, 171–182. Chenglin, Z., Xueping, H., 2016. Research on the impact of natural resource endowment on regional ecological efficiency. J. Dalian Univ. Technolgy: Soc. Sci. 37 (3), 19–26. Ciccone, A., Hall, R.E., 1993. Productivity and the Density of Economic Activity. National Bureau of Economic Research.

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