Spatial spillover effects of climate policy: evidence from the carbon inclusion scheme in the Greater Bay Area in China

Exploring the effects of energy quota trading policy on carbon emission efficiency: Quasi-experimental evidence from China

Exploring the spatial spillover effect of industrial structural upgrading on carbon emission efficiency: Evidence from China.

Clean energy is an essential means to limiting carbon emissions and improving economic transformation, and a market-oriented financial structure is the inevitable result of the deepening of supply-side financial reforms. Exploring whether clean energy enhances carbon emission efficiency (CEE) through financial structural adjustment is essential in formulating policies intended to achieve the dual goals of “carbon peaking” and “carbon neutrality”. As part of the evaluation of China’s provincial CEE using panel data of 30 provinces from 2000 to 2019, this paper adopts an improved nonradial directional distance function (NDDF), while empirically analyzing the influence of clean energy and a market-oriented financial structure on CEE using a spatial econometric model. The results indicate the following findings: (1) The provincial CEE in China is characterized by significant spatial autocorrelation. (2) A 1% increase in the integration of clean energy and a market-oriented financial structure leads to a 0.0032% increase in the local CEE and a 0.0076% increase in neighboring regions’ CEE through the spatial spillover effect. Clean energy can efficiently enhance CEE through the stock market, while it has a passive impact through bank credit. (3) The interactive effect between clean energy and a market-oriented financial structure varies according to the provincial CEE. From the 25th to the 90th quantiles, the role of clean energy in promoting CEE through the capital market is very significant, while clean energy inhibits CEE through bank credit in most provinces. Therefore, China’s clean energy development will bolster its competitiveness in the global market through a market-oriented financial structure that will bring economic development and environmental pollution into balance and provide a theoretical foundation for China’s double carbon reduction.

The new quality productive forces is an advanced force committed to high-quality development and a significant driving force in achieving the "double carbon goal." To explore the impact of the new quality productive forces on carbon emissions in the Beijing-Tianjin-Hebei Region and enhance the positive effects of region's new quality productive forces on carbon emission levels and efficiency， this study aims to promote high-quality development in the Beijing-Tianjin-Hebei Region. Initially， based on urban panel data from the Beijing-Tianjin-Hebei Region from 2007-2022， in accordance with the industrial structure and current policies of the Beijing-Tianjin-Hebei region， a comprehensive evaluation method is utilized to calculate the index of new quality productive forces in the region， and the data envelopment analysis method is employed to measure the carbon emission efficiency. The development patterns and correlations between these two indices are analyzed across both temporal and spatial dimensions. Subsequently， an econometrics model is applied to examine the impact of new quality productive forces on carbon emissions and emission efficiency， and a mediation effect model is established to assess the mechanisms by which new quality productive forces affect carbon emissions through industrial structure and enhance carbon emission efficiency through technological innovation. The study produced several interesting results： ① The new quality productive forces can significantly reduce carbon emissions and improve carbon emission efficiency in the Beijing-Tianjin-Hebei Region， with results remaining robust after a series of tests， including winsorization test， alteration of control variables， and changes in estimation methods. ② After passing the mediation effect test， it is realized that new quality productive forces can influence carbon emissions through the regional industrial structure and enhance carbon emission efficiency by elevating the level of scientific and technological innovation. ③ Classifying cities in the Beijing-Tianjin-Hebei Region into resource-based cities and low-carbon pilot cities and conducting stratified regression analyses shows that the impact of new quality productive forces on carbon emissions and emission efficiency exhibits significant heterogeneity among different types of cities， providing crucial references for differentiated governance in the region. Based on the conclusions above， the study proposes recommendations from three aspects： constructing a coordinated development pattern of carbon emission reduction and efficiency， promoting the governance process of carbon emission reduction with the new quality productive forces， and developing and governing methods tailored to local conditions. These suggestions can provide new research perspectives and practical guidance for China to achieve the "double carbon goal."

Spatial responses of ecosystem services to carbon emission efficiency based on spatial panel models in the Yangtze River Economic Belt

High-quality tourism development under the “double carbon” target (the peaking of carbon emissions and achievement of carbon neutrality) is an important path to achieving low-carbon emissions in the tourism industry and is vital for improving the industry’s carbon emissions efficiency. Using spatial and temporal panel data for 11 prefecture-level cities in Jiangxi Province from 2000 to 2020, a spatial Durbin model and a threshold model were constructed to assess the spatial spillover and threshold effects that high-quality tourism development has on the carbon emission efficiency of the tourism industry. The three key results were as follows. (1) There is a non-linear relationship between the carbon emission efficiency of tourism and the high-quality development trend of tourism, with differences in spatial distribution. (2) Coordinated development, green development, and open development all have significant positive direct effects on the carbon emission efficiency of tourism. Innovation-driven and coordinated development have a positive spillover effect on the carbon emission efficiency of tourism. In contrast, green development, open development, and shared results have a negative spatial spillover effect. (3) When the scale of the tourism economy crosses the first threshold in the second stage and the structure of tourism investment crosses the second threshold in the third stage, the ability of the tourism quality development to enhance the tourism carbon emission efficiency is the largest. When the tourism investment structure and tourism carbon emission intensity cross a single threshold, the role of the tourism quality development level in enhancing the tourism carbon emission efficiency decreases. Accordingly, three types of countermeasures are proposed: solving development problems, tapping into positive spillovers, and scientifically describing the impact of thresholds. The ultimate goal of this is to provide theoretical references and innovative ideas for promoting green, low-carbon, and high-quality development of tourism in Jiangxi Province and elsewhere.

Does digital investment affect carbon efficiency? Spatial effect and mechanism discussion

The impact of carbon emissions trading policy on carbon emission efficiency in Chinese cities: Evidence from a quasi-natural experiment

Based on the super-efficiency SBM model with unexpected output, this study calculated the urban carbon emission efficiency in order to study the regional differences and spatial spillover effects of urban carbon emission efficiency in the Yangtze River Delta. From 2001 to 2020, the highest average of urban carbon emission efficiency in the Yangtze River Delta was only 0.698, with a low overall level and a flat "Inverted-U" evolution trend. Pure technical efficiency became the main factor driving the change in carbon emission efficiency. The Gini coefficient was relatively high, fluctuating in different years and still dominated by growth. The regional differences in urban carbon emission efficiency increased. The contribution of inter group differences among the four provinces （city） was the main source of the differences in urban carbon emission efficiency. The Moran's I index was positive, which indicated that the positive autocorrelation of carbon emission efficiency was significant, and the spatial spillover effect was obvious. The population density, economic level, and technical level had significant effects. The direct, indirect, and total effects of industrial structure, road network density, and foreign capital intensity on carbon emission efficiency did not pass the significance test. Based on the perspective of factor input-output, the study accurately measured urban carbon emission efficiency in the Yangtze River Delta, identified the causes of the differences in urban carbon efficiency, and introduced the "spatial spillover effect" to analyze the mechanism of carbon emission efficiency. This study compensated for the lack of research on the urban carbon emission efficiency in the Yangtze River Delta and provided quantitative support and scientific inspiration for the low-carbon development.

Against the backdrop of low-carbon development, it is imperative to cultivate a modernized industrial system and new development model. Industrial collaborative agglomeration (ICA) between manufacturing and producer services may offer an opportunity to aid carbon reduction in this scenario. Using balanced panel data of China's 30 provinces and municipalities from 2008 to 2019, this paper attempts to investigate the influences of ICA on carbon emission efficiency (CEE), its heterogeneous effects, impact mechanisms, and spillover effects. Our main findings can be concluded as follows: (1) There is a U-shaped relationship between ICA and CEE; namely, ICA will inhibit first and then promote CEE. (2) The heterogeneity results further indicate that this U-shaped relationship is significant in the eastern area while there exists an inverted U-shaped relationship between ICA and CEE in the western area; however, the influence of ICA on CEE is not significant in the central area. (3) More deeply, the mechanism identification uncovers that industrial structure upgrading and green technological innovation are important channels through which ICA affects CEE. (4) Importantly, we unfold that ICA in the local area has spatial spillover effects; namely, it will influence CEE in neighboring areas, which also presents a U-shaped relationship. These findings provide not only new insights into understanding the environmental effects of ICA but also helpful inspiration for regional policymakers to scientifically formulate industrial development policies and effectively implement carbon emission control actions.

Coordinating development of land urbanization and population urbanization (CDLUPU) to enhance carbon emission efficiency (CEE) is a critical challenge for developing countries experiencing accelerated urbanization. The coupled coordination model and super efficiency SBM are employed to estimate the levels of CDLUPU and CEE across 276 prefecture-level cities from 2010 to 2021. Furthermore, we utilize kernel density estimation and Spatial Durbin Model (SDM) to explore the spatio-temporal distribution characteristics and spatial effects. The results indicate that CDLUPU levels exhibited a sustained upward trend with diminishing regional disparities, whereas CEE displayed a pattern of initial growth followed by decline. Spatial analyses revealed a consistent gradient structure for both CDLUPU and CEE, characterized by radiation decay from southeastern coastal hubs toward interior hinterlands. CDLUPU exerts a significant positive direct impact and spatial spillover effect and indicates that the spillover effects on peripheral regions are substantially stronger than local effects. Regional heterogeneity analysis reveals that CDLUPU negatively affects CEE in eastern China, the Yangtze River Delta (YRD) is more pronounced, but it positively impacts central and western China, as well as Beijing–Tianjin–Hebei (BTH) and Chengdu–Chongqing (CY). Regarding indirect effects, eastern China shows significant positive impact on CEE, and similarly in the YRD. However, central China exhibits a negative effect, whereas BTH shows the opposite trend. Western China and CY show statistically insignificant results. This study offers policy insights for China to coordinate new urbanization strategy and achieve the “dual carbon goal”.

What drives urban carbon emission efficiency? – Spatial analysis based on nighttime light data

Blue sky defense in low-carbon pilot cities: A spatial spillover perspective of carbon emission efficiency

Studying urban carbon emission efficiency is vital for promoting city collaboration in combating climate change. Prior research relied on traditional econometric models, lacking spatial spillover effects understanding at the urban scale. To provide a more comprehensive and visually insightful representation of the evolving characteristics of carbon emission efficiency and its spatial clustering effects and to establish a comprehensive set of indicators to explore the spatial spillover pathways of urban carbon emission efficiency, we conducted an analysis focusing on 42 cities in the middle reaches of the Yangtze River. By employing the index decomposition method, the super-efficiency SBM model, spatial autocorrelation analysis, and the spatial Durbin model, the study calculates the urban carbon emission efficiency from 2011 to 2019 and analyzes the spatial spillover effects and influencing factors of urban carbon emission efficiency. The main conclusions are as follows: (1) Jiangxi Province displayed stable urban carbon emission efficiency evolution, while Hubei and Hunan showed significant internal disparities. (2) Positive spatial correlation exists in urban carbon emission efficiency, with an imbalanced distribution. (3) Various factors influence urban carbon emission efficiency. Technological innovation and economic development have positive direct and indirect impacts, whereas industrial structure, urbanization, population, and energy consumption have negative effects. Spatial spillover effects of vegetation coverage are insignificant. These methods and findings offer insights for future research and policy formulation to promote regional sustainable development and carbon emission reduction.

Confronted with the concurrent challenges of economic advancement and environmental management, this study explores whether implementing Intellectual Property Demonstration Policies (IPDP) can be a covert force in enhancing carbon emission efficiency. Utilizing panel data from 280 prefecture-level cities in China over the period 2007–2019, we employ a quasi-natural experimental design, incorporating multiple-period difference-in-differences models, mediation effect models, and spatial Durbin difference-in-differences models to assess the impacts of IPDP on carbon emission efficiency, its mechanisms of action, and its spatial spillover effects. The regression results of the multi-period difference-in-differences model reveal a statistically significant enhancement in carbon emission efficiency due to IPDP, with an impact coefficient of 0.044. Through heterogeneity tests, it is observed that the influence of IPDP on carbon emission efficiency varies based on regional characteristics, carbon emission levels, and the extent of marketization. The mediation effect model demonstrates that IPDP enhances carbon emission efficiency by fostering green technological innovation and facilitating the transformation of industrial structures. Furthermore, the spatial Durbin difference-in-differences model illustrates that IPDP positively influences the carbon emission efficiency of neighboring regions, indicating favorable spatial spillover effects. Notably, the indirect effect coefficients in the geographical distance matrix, economic distance matrix, and economic-geographical nested matrix are calculated as 0.673, 0.250, and 0.386, respectively. These findings offer compelling theoretical and empirical support for strengthening the intellectual property framework to optimize its environmental impact.