**1. Introduction**

Green development is a necessary condition for sustainable development, which is essential for a better life in the future. Green development takes account of both development quality and development efficiency, as it is focuses on the efficient use of resources and comprehensive environmental protection. However, green development requires the harmonious and unified development of the economy, the ecology, and the society, of which green economy development is particularly important. In 1989, Pearce's "Green Economy Blueprint" first mentioned the "green economy" and claimed that the establishment of an "affordable economy" required that economic development be related to resource and environmental carrying capacities and that national economic balance should include the costs related to the polluted environment and resource waste [1]. In 2011, the UNEP pointed out that green economies needed to be low-carbon, resource-saving, and socially inclusive, should promote social equality for the benefit of mankind, and reduce environmental risks and ecological scarcities [2]. In 2012, the World Bank pointed out that green economies required environmentally friendly and highly socially inclusive economic growth so as to both protect and improve the ecological environment and make full use of natural resources to ensure the coordinated development of the society, the economy, and the environment, with economic growth being focused

on sustainable development [3]. However, the traditional 'black' development concepts of unilaterally pursuing the maximization of economic benefits is deeply rooted in society, which highlights the current contradictions between economic growth, social construction, and ecological protection, such as severe haze, sandstorms, greenhouse benefits, and El Niño disasters [4]. To develop green economies further while maintaining economic development, it is necessary to first measure current economic development, explore the impacts of green economy developments, and develop methods to ensure sustainable green economy development.

To measure the current state of green economy development, this paper constructs a green economy development evaluation index system based on the drivers, pressures, state, impact, response model of intervention (DPSIR) that is combined with an entropy weight-TOPSIS-coupling coordination degree model.

In recent years, green economy development evaluation research has mainly focused on the construction of green economy development evaluation index systems and associated measurement methods. Many international authorities have developed green economy development evaluation index systems. For example, the United Nations Environment Program (UNEP) [5] established a green economy evaluation index system, The Global Green Growth Institute (GGGI) [6] built a green economy indicator evaluation system from the perspective of national development, social status, resource consumption and environmental status, , the World Commission for Environment and Development (WCED) [7] established an urban green development evaluation index system that included urban green coverage and tertiary industry, the OECD (OECD) [8] constructed a green growth indicator system to reflect sustainable economic development indicators, and the EU [9] conducted comparative selection indicator analysis. As these economic evaluation index systems mainly involved the integration of green economy development capabilities from different countries, it is not possible to specifically evaluate the regional economic development status in each region. However, research scholars have evaluated green economy development by selecting green GDP [10–12] green economy efficiency [13–15], green economy indexes [16,17], and other indicators. While these green economy evaluation index systems were able to better measure the development of green economies in certain areas, they were only able to describe green economy development levels on a macro level, and were unable to deeply analyze the impact of the economic, resource, environmental, social, and technological factor interactions on green economy development. To overcome these problems, in this paper, a DPSIR is used to construct a green economy development evaluation index system that accounts for the internal mechanisms and allows for in-depth evaluations of green economy development in a certain region or multiple regions.

Green economy evaluations need to have objective, practical, and scientific measurement methods. To date, several research methods have been developed. For example, Beijing Normal University adopted a Delphi method to determine indicator entropy so as to assess the green economy differences in different provinces and cities in China [4], Zeng, and Bi used principal component, clustering, and multiple linear regression analyses to analyze the horizontal and vertical dimensional development of the green economy in 30 provinces in China, from which it was found that the overall development was good, but the inter-regional two-level differentiation was serious [18], and Yi and Zhang used an entropy weight method and a difference coefficient to study the green economy level in 30 provinces in China in 2015, and found significant regional differences [19]. While these methods were able to comprehensively elucidate the regional green economy developments, they were unable to fully reveal the internal development restrictions. Na used an SBM model to measure provincial-level green economy efficiency in China from 1995 to 2012 and found that the regional green economy efficiency differences were large, and that the energy and carbon dioxide emissions were the key factors restricting the green economy efficiency [14], and Xiaoyun W used DEA-BCC and DEA-Malmquist models to analyze the green economy efficiency of 285 cities in China from 2004 to 2012, and found that technological progress was the main driver restricting urban green economy development efficiencies [15]. The above methods were able to identify the objective factors restricting the

development of the green economy to some extent by analyzing the multi-input and output efficiencies of the complex green economy development system. However, there has been little research on green economy development trends and the degrees to which the various factors affect green economy development. Therefore, this paper adopted an entropy-TOPSIS-coupling coordination model that first vertically measures the green economy development trends and then horizontally analyzes the coordination between the various internal factors to determine the factors that are restricting green economy development.

#### **2. Construction of the Green Economy Development Evaluation Index System**

Green economy development systems are complex systems as they are influenced by economic, social, energy, environmental, and technological factors. Therefore, when constructing measurement indicators, it is necessary to ensure that each evaluation index truly reflects the state of the regional green economy development, takes account of the rationality of each evaluation index, and has sufficient applicability over a considerable period of time.

The DPSIR conceptual model, which evolved from the PSR conceptual model, was first proposed by the European Environmental Agency (EEA) in 1999 [20] to provide a basic and effective model for research into the resource environment, the social economy, and other issues. As shown in Figure 1, the DPSIR model is a complex circulatory system [21] that divided into the resource, environmental, societal, economic, and technological indicators that affect green economy development, which are then assessed into five categories; drivers (D), pressures (P), state (S), impact (I), and response (R); to take account of the indicator interactions. The model mainly emphasizes the causal relationship between human economic activities and environmental changes: human production and life drive economic development, but also bring pressure to the local ecological environment, changing the original state and nature of resources and environment; changes in the environment will also affect human life and urban development. In order to maintain the sustainable development of society, humans will take measures to respond to these changes. This model has been used to analyze regional green economy development [22], regional adaptations to climate change [23], economic development and environmental warnings [24], and economic development and carbon emissions [25], and has achieved good results.

**Figure 1.** DPSIR green economy development evaluation system framework.

Natural resources, population, science and technology, culture, and education determine the level of regional economic development from the aspects of regional resource richness, production and consumption capacity, productivity development level, and human resource quality. The development of green economy is an inevitable choice for the sustainable development of economy, society, and ecological environment [26,27]. Green economy development is needed to improve the conflict among economic development and energy consumption, resource utilization, and environmental protection [28,29].

Shi, B. and Yang, H [16] selected 85 indicators to evaluate the urban green economy from the perspectives of economy, society, and resources. Shi, L and Xiang, X [25] from the driving force-pressure-state-impact-response selected 19 indicators including regional GDP, unit GDP energy, carbon emissions per unit of GDP, public perception of low-carbon cities, and forest coverage to evaluate urban low-carbon economy. Shen Juqin and Sun Yue [30] combined with the DPSIR model to select 28 indicators for evaluation of fixed asset investment, energy consumption, green coverage, per capita disposable income, and sewage treatment rate from the factors affecting regional green GDP development.

This paper selects 26 indicators that affect regional green economy development from five aspects: economy, society, environment, energy and technology. Because the impact is difficult to measure, to avoid uncertainty in the "I-impact" factor index in the DPSIR model criterion layer construction, a criterion layer based on the DPSR, namely the drivers-pressures-state-response, is established. Therefore, the 26 indicators are divided into economic driving force D1, social driving force D2, energy pressure P1, environmental pressure P2, environmental state S1, and science and technology response R1, total of six blocks, to measure regional green economy development.

The choice of economic driving indicators selected fixed asset investment, foreign trade volume GDP growth rate, per capita GDP, per capita disposable income of urban residents, and household consumption level from the perspectives of internal and external, national individual and income expenditure as the basis for measuring the standard economic driving force.

The choice of social driving force indicators selected the family size, per capita water use, per capita urban road area, and 10,000-person bus ownership of the household registration population from the population problem and infrastructure security level that have significant impact on social development.

Energy pressure indicators were selected by analyzing the relationship between economic development and energy consumption. The energy consumption and power consumption of the two major factors that constrain economic development are selected for measurement.

Environmental pressures reveal the industrial production that is the most vulnerable to the environment during the economic development process. Therefore, industrial wastewater discharge, industrial smoke (powder) dust emissions, industrial solid waste comprehensive utilization, SO2 emissions per unit of GDP, and chemical oxygen demand emissions are selected as the basis for measurement.

The choice of environmental status indicators is mainly based on the perspective of green life. The pollution-free treatment rate of living garbage, the per capita park green area, the forest coverage rate, and the green area coverage of the built-up area were selected as the basis for measurement.

As an important part of promoting green development of science and technology, science, and technology response indicators should be selected from the process of investment in science and technology innovation, output, and application. Therefore, R&D expenditures accounted for the proportion of GDP, the proportion of R&D personnel with high education (master's degree or above), the number of patents granted by 10,000 people, the proportion of secondary industry to GDP, and the proportion of tertiary industry to GDP as the basis for measuring scientific and technological response. The specific indicator system is shown in Table 1.





The standard economic driving force indicators are fixed asset investments, foreign trade, GDP growth rate, per capita GDP, urban resident per capita disposable income, and household consumption level. The basic household scale and infrastructure security levels are assessed by the social driver indicators; household registration family size, per capita water consumption, per capita urban road area, and buses per 10,000-people. The current economic development stage is assessed using the energy pressure indicators, which include energy consumption and power consumption constraints, with GDP energy consumption and unit GDP power consumption being the pressure measures. The environmental pressure indicators are; industrial wastewater discharge, industrial smoke (powder) dust emissions, comprehensive industrial solid waste utilization, SO2 emissions per unit of GDP, and chemical oxygen demand emissions. The environmental state indicators that measure the environmental green status are: the pollution-free treatment rate for living garbage, the per capita park green area, the forest coverage rate, and the green area coverage in built-up areas. As scientific and technological innovation can promote green economic development, the indicators are: R&D expenditures as a proportion of GDP, the proportion of R&D personnel with higher education (master's degree or above), the number of patents granted per 10,000 people, the proportion of secondary industry to GDP, and the proportion of tertiary industry to GDP.

### **3. Regional Green Economy Development System Research Model**
