Assessment of green economic efficiency in China using analytical hierarchical process (AHP)

Evaluating the influence of green growth, institutional quality and financial inclusion on financial stability: evidence by sustainable finance theory

Financial stability is essential for economic growth because it fosters confidence and trust and promotes investment in green development. However, it is a dilemma for the world economies to create an equilibrium between financial stability and environmental sustainability. In the extent of these challenges, the present study aims at grabbing the link of financial inclusion to attain financial stability. Further, the present study investigates the association of institutional quality, renewable energy, green growth, environmental sustainability, and financial inclusion with financial stability. Two basic econometric models are applied that focused on the basic and interaction term outcomes. In addition, principal component analysis (PCA) is analyzed to design an index for five proxies of financial inclusion. Additionally, the research inspected the interaction term of institutional quality and financial inclusion (FIN*INSQ) and determined the multiplied impact on financial stability in a separate model. This research employed the linear autoregressive distributed lag approach from 1990 to 2020 for long- and short-term dynamics. Theoretically, the research supports the sustainable finance and financial development theory. Hence, results showed that financial inclusion and institutional quality are positively associated with financial stability, while green growth, environmental sustainability, and renewable energy mechanisms are achieved through financial stability. Following our findings, the government should establish consistency between financial development and economic policies to maintain financial instability and ensure financial soundness. Furthermore, countries require viable financial institutions prioritizing green growth and institutional quality to achieve financial stability and long-term development.

Study on the spatial distribution of urban carbon emissions at the micro level based on multisource data

Global warming is currently an area of concern. Human activities are the leading cause of urban greenhouse gas intensification. Inversing the spatial distribution of carbon emissions at microscopic scales such as communities or controlling detailed planning plots can capture the critical emission areas of carbon emissions, thus providing scientific guidance for intracity low-carbon development planning. Using the Sino-Singapore Tianjin Eco-city as an example, this paper uses night-light images and statistical yearbooks to perform linear fitting within the Beijing-Tianjin-Hebei city-county region and then uses fine-scale data such as points of interest, road networks, and mobile signaling data to construct spatial characteristic indicators of carbon emissions distribution and assign weights to each indicator through the analytic hierarchy process. As a result, the spatial distribution of carbon emissions based on detailed control planning plots is calculated. The results show that among the selected indicators, the population distribution significantly influences carbon emissions, with a weight of 0.384. The spatial distribution of carbon emissions is relatively distinctive. The primary carbon emissions are from the Sino-Singapore Cooperation Zone due to its rapid urban construction and development. In contrast, carbon emissions from other areas are sparse, as there is mostly unused land under construction.

Spatiotemporal differentiation and convergence characteristics of green economic efficiency in China: from the perspective of pollution and carbon emission reduction

Accurate quantification of pollution and carbon emission reduction policies, as well as analysis of green economic efficiency (GEE), are of great significance to accelerating green economic development in China and contributing to pollution prevention and carbon peaking. Using data from 2006 to 2022, this study incorporates pollution and carbon emission reduction policies into the evaluation system, and uses a model with slacks-based measures and a directional distance function (SBM-DDF) to calculate the GEE of 30 provinces. The Dagum Gini coefficient, kernel density estimation, and spatiotemporal convergence analysis are used to analyze the spatiotemporal differentiation and convergence characteristics of GEE. The findings show that the strengths of the pollution and carbon emission reduction policies are increasing but vary greatly among the provinces. China's overall GEE has a time trend with the characteristics of "decline-fluctuation-stable." The Dagum Gini coefficient reveals the relative differences between the major regions. Both the intra-regional and inter-regional differences tend to widen over time and the latter explains most of the sources of the overall differences. Kernel density estimation shows that the absolute differences between the provinces are generally widening, whereas the absolute differences between the provinces in the central and western regions are smaller than those in the eastern region. No obvious σ convergence characteristics exist in the country overall and the three major regions, but β convergence characteristics are present in each region. The factors affecting changes in the GEE of each region are not the same. The study suggests that the China should further improve the implementation of pollution and carbon emission reduction policies, pay attention to the regional differences and convergence issues of GEE, and promote the coordinated development of green economy in different regions. This study innovatively quantifies the policies related to pollution and carbon emission reduction, providing empirical evidence for understanding the performance of pollution and carbon emission reduction policies in various regions. Furthermore, this study incorporates policies as inputs into the GEE evaluation system, reveals the spatiotemporal differentiation of GEE, thereby providing reference for green economic transformation and sustainable development.

Analyzing inclusive green growth in China: a perspective of relative efficiency

Inclusive Green Growth (IGG) has important reference value for China's ecological civilization construction and transformation of economic development. Therefore, this study assesses China's IGG level from the perspective of relative efficiency. The IGG efficiency (IGGE) was measured at the provincial level in China from 2000 to 2020 by using Super-Epsilon-Based Measure (EBM) model that considers undesirable outputs. The spatiotemporal pattern of IGGE was analyzed by kernel density estimation and spatial autocorrelation. The results indicate a fluctuating trend from 2000 to 2020 for the IGGE of China, and significant differences between regional and interprovincial IGGE were observed. On average, the eastern region presented the highest efficiency, while the level in the central regions was lowest. There is a positive spatial autocorrelation in the IGGE distribution, and the agglomeration of spatial distribution fluctuated during the study period. The IGGE has spatial spillover effects at the provincial level according to the spatial Durbin model. Among the influencing factors, the spatial spillover effects of industrial structure, government administrative capability, and industrialization level are significant. The regression results also confirm the Environmental Kuznets Curve effect between IGG and economic growth in China. Finally, some implicit policies can be established based on the empirical analysis.

Role of the e-exhibition industry in the green growth of businesses and recovery

In this paper, a survey and two multi-attribute decision-making (MADM) models have been employed to explore critical success factors of e-exhibition in 30 Chinese provinces that is divided into 8 different regions. The research findings showed that in China, the most important success factors of e-exhibition to have green economic recovery are the presence of International collaboration (0.592), green culture (0.490), and visitor’s attitude (0.439). Furthermore, “Beijing and Tianjin” is the most ideal region to promote e-exhibition in China. South Coast region ranked in second place as the most appropriate region for e-exhibition. The least ideal region of China for e-exhibition is the Southwest region that is less developed compared to other regions of China. The major practical policies are the enhancement of international cooperation to hold an e-exhibition, use of electronic exhibition capacities (synchronous and asynchronous) and creating social sustainability awareness through the media and social network.

A Method for Evaluating the Green Economic Efficiency of Resource-Based Cities Based on Neural Network Improved DEA Model

In this study, we use BP neural network to improve the DEA model to conduct in-depth research and analysis on the method of green economic efficiency evaluation of resource-based cities. The traditional DEA cannot make ranking and analysis of effective units, which affects the accuracy of empirical analysis. Accordingly, the BP-DEA model is introduced to further conduct a comparative eco-efficiency analysis of relatively effective provinces. In this study, the optimal inputs and outputs are calculated by DEA, and further, the BP neural network is used to fit the functional relationship between the optimal inputs and outputs, and by adding variables, the trained neural network can be used for the prediction of the optimal outputs. In this study, the BP-DEA model is used to empirically investigate the temporal evolution trend, spatial differences, and efficiency differences in eco-efficiency. Meanwhile, breaking through the limitation that DEA can only calculate regional efficiency values, this study combines the Malmquist index to compare and decompose the eco-efficiency of different provinces to analyze the sources of total factor productivity changes. The results show that the method can clarify the gap between the actual operation of each indicator and the reference point; it can identify how much room for improvement still needs to be made for each indicator, and it can also determine whether each city should be rewarded or penalized and its specific amount. Finally, based on the evaluation of eco-efficiency and the main constraints, corresponding policy recommendations are proposed. Finally, based on the evaluation results of the BP-DEA method, this study analyzes the overall efficiency improvement of cities in the two study areas in three dimensions: urbanization construction, ecology, and economic development put forward seven types of urban efficiency improvement and propose targeted urban development suggestions according to regional characteristics.

Corporate Co-Agglomeration and Green Economy Efficiency in China

This paper uses panel OLS, IV, and system GMM methods to empirically study the effects of manufacturing and producer service corporate co-agglomeration on green economy efficiency (GEE) in China. Chinese panel data from 2000 to 2019 are collected to assess the GEE and co-agglomeration degrees. The regression results show that there is an “inverted U-shaped” relationship between co-agglomeration and GEE. However, regional heterogeneity is found in the effects of corporate co-agglomeration on GEE. The mediating analysis indicates that corporate co-agglomeration could increase GEE through business entrepreneurship and innovation entrepreneurship. Variables such as transportation infrastructure, human capital, foreign direct investment, and environmental regulations are also found to have an elevating effect on GEE, whereas local fiscal expenditure on environmental protection has little effect. The findings in this paper indicate that entrepreneurship plays an important role in the process of co-agglomeration impacting GEE which differs in different regions and thus provide references for corporate and regional sustainable development.

Measuring green development level at a regional scale: framework, model, and application

In this study, we propose and construct a novel model that measures regional green development level based on the "three-circle" conceptual framework for green development. Using Jiangsu Province in eastern China as a case study, the spatial-temporal characteristics and dynamics of the green development level from 2000 to 2020 were evaluated using a multi-source dataset at the grid-cell level. Our results show that (1) the analytical hierarchy process-based model proposed herein has higher reliability in terms of the development level measurement than principal component analysis and the entropy weight method. In addition, the average score of green development in the study area was approximately 0.53. Spatially, the green development level in the eastern coastal areas of the study area was found to be generally higher than in other regions, while that in southwestern regions is relatively low. In terms of sub-regions, the green development level scores of the study area have been ranked as follows: middle Jiangsu > southern Jiangsu > northern Jiangsu. (2) It was observed that the gravity center of the green development level can be divided into three stages during the study, with a whole had shifted to the north. (3) For most cities in Jiangsu, the green development level initially increased at first, then declined, and then increased again. (4) In the future, the green development level of Jiangsu Province should pay more attention to promoting regional coordinated development and relationships between society and the environment under rapid economic development.