An empirical study on spatial spillover of carbon emissions and financial development based on provinces data and spatial panel econometrics model

The paper explores the spatial spillover of carbon emissions and influencing factors based on spatial econometric model using panel series data over the period 2000-2012 in case of China. The results confirm that carbon emissions have spatial dependence, spatial spillover effects of provincial carbon emissions and influencing factors are obvious; Economic growth, industrial structure and export dependence coefficients are positive, technology progress on reducing carbon emissions growth effects is remarkable, energy prices effects failed to pass the test of significance. The spatial spillover effects of carbon emissions and related influencing factors among neighbor provinces must be considered when government departments formulate policies and development planning. The paper opens up new insights for China decrease China's carbon emissions from different dimension as a whole.

New spatial econometric techniques and applications in regional science

The study investigates China's provincial carbon emissions if there is spatial dependence by using spatial panel econometric models and analyzed the functional mechanism industrial restructuring in the growth of carbon emissions. The empirical results show that Chinese provincial carbon emissions has dependencies, showing local relevant spatial agglomeration characteristics in space, near the provincial spatial spillover effects of carbon emissions significantly; adjustment of industrial structure on carbon emissions elasticity coefficient is significantly positive, provincial economic growth to adjacent provincial carbon emissions have significantly reduced the effect of provincial industrial structure adjacent provincial carbon emissions growth with the promotion of the role; technological advances inhibition of carbon emissions cannot be ignored. Government departments in the development of relevant industry development plan should aim to strengthen cooperation and exchanges in neighboring regions, and improve the industrial structure adjustment policies taking into account the spatial effects of effector mechanisms

The issue of carbon emissions in China’s interprovincial border regions is crucial for achieving the nation’s carbon peaking and neutrality goals. This study aims to comprehensively analyze the spatiotemporal evolution characteristics of carbon emissions in China’s interprovincial border counties from 2005 to 2017, identify their key influencing factors using spatial econometric models, and elucidate the associated spatial spillover effects. Within the STIRPAT model framework and using GIS as a platform, datasets of county-level carbon emissions and spatial weight matrices for China’s interprovincial border areas were analyzed. Maps and spatial data analysis methods were employed to identify spatiotemporal patterns of carbon emissions in these regions. Additionally, the spatial panel Durbin model was utilized to uncover the driving mechanisms of key controlling factors. The findings indicate that although border counties contribute only marginally to China’s total emissions, they exhibit stronger growth compared to non-border counties. Notably, the average emissions in eastern border regions are comparable to the combined emissions of central and western regions. Spatial analysis reveals higher emissions in the north and east, with a trend of bidirectional expansion. While overall disparities are decreasing, they remain significant. High-high (HH) and low-low (LL) clusters demonstrate significant positive spatial correlation. Carbon emissions in border counties show substantial positive spatial spillover effects. Various factors influence emissions differently: population tends to suppress local emissions, while economic growth increases emissions both locally and in neighboring areas. Technological advancement reduces emissions in both local and adjacent regions, and industrial structure plays a driving role. This research provides novel insights into the carbon emission dynamics of border regions and offers a scientific basis for formulating differentiated emission reduction policies.

Impact of urbanization and real estate investment on carbon emissions: Evidence from China's provincial regions

Research on the impact of multi-dimensional urbanization on China's carbon emissions under the background of COP21

Since the global transition into the industrial era, the rapid economic growth has been accompanied by massive energy consumption, leading to a dramatic increase in global carbon emissions and severe ecological damage, which poses significant threats to human existence and economic development. To formulate effective carbon reduction policies, extensive research has been conducted on carbon emissions. Given the significant spatial spillover effects of carbon emissions, spatial econometric models can provide more accurate modelling and analysis in this area. As a result, spatial econometric methods have been widely applied in the study of carbon emissions. This paper systematically summarises the key achievements of spatial econometrics in the field of carbon emissions, including the spatial distribution and evolutionary characteristics of carbon emissions, their influencing factors, and the integration of spatial econometric models with other methodologies. Based on the existing research, the paper highlights the limitations of the current spatial econometric models in the field of carbon emissions, such as issues for model improvement, data quality, regional heterogeneity and dynamic characteristics of carbon emissions, policy limitations, and insufficient interdisciplinary collaboration as well as methodological integration. Finally, we provide an outlook for future research, focussing on addressing the relevant issues and expanding the global perspective regarding regional carbon emissions.

From the perspective of spatial geography, this paper verifies the spatial dependence of China’s provincial carbon emissions. The contribution of impact factors with different fields of view to carbon emissions’ growth is estimated based on the spatial panel data model, t. The study found that during 2000–2015, China’s energy-related carbon emissions in the provinces were dependent on the spatial, and the spatial spillover effect of carbon emissions and its influencing factors in the neighboring provinces are obvious. It was also found that economic growth, industrial structure, financial development, and urbanization rates are positive, and the effect of the population and technological progress on reducing carbon emissions is significant. The effect of source price, export dependence, and fiscal decentralization on carbon emissions’ growth did not pass a significance test. In the formulation of carbon emission-related policies and development plans, the government must consider the effect of the influencing factors affecting the carbon emissions in the adjacent area and combine the carbon emissions and spatial spillover effect of the related factors in order to reduce carbon emissions in the time dimension and the spatial dimension of China as a whole.

In China, achieving financial agglomeration necessitates both the gathering of financial resources and the reduction of carbon emissions and the interrelation between these two goals is significant. In this research, sophisticated econometric models are applied to examine the correlation between financial agglomeration and per capita carbon emissions in China, such as spatial econometric, mixed OLS, and stationary panel models. The research sample is composed of data from 30 provinces and cities in China from 2010 to 2020 and examines both temporal and spatial distributions of these two factors and how they influence each other. The direct effect of financial agglomeration on carbon emission is analyzed through spatial panel model, while indirect effect is analyzed by examining the role of industrial structure upgrading as a mediating variable through mediating effect model. This study also looks at how these effects vary regionally, both directly and indirectly. Generally, the study discovered that financial agglomeration and per capita carbon emissions have significantly positive spatial autocorrelation coefficient in all provinces and cities in China, indicating path dependence and spatial spillover. In terms of distribution trends, financial agglomeration shows an upward trend over time, while per capita carbon emissions grew faster in the early stage, but gradually achieves a steady decrease in recent years. In terms of the impact of financial agglomeration on carbon emissions, the relationship between financial agglomeration and per capita carbon emissions is in the form of "inverted U-shaped." Financial agglomeration indirectly influences the intensity of per capita carbon emissions through the advanced industrial structure, which acts as a mediator. In the test of industrial structure serving as a mediating variable, different regions exhibit different impacts due to regional heterogeneity, with a clear distinction between the central region and the eastern and western regions.

Regional disparity, spatial spillover effects of urbanisation and carbon emissions in China

Using a two-way panel fixed effects model and a spatial econometric model, an empirical study examines the effect of FDI on carbon emissions based on inter-provincial spatial panel data in China from 1998 to 2016. The study shows that: Overall, there seems to be a significant positive spatial spillover effect on carbon emissions, and there is a high-high aggregation of carbon emissions across provinces. Among them, the direct effect and spatial spillover effect of FDI on carbon emissions have significant temporal and regional differences. From the analysis of regional heterogeneity, the direct impact of FDI on carbon emissions in the province exists only in the central region; the spatial spillover effect of carbon emissions in other provinces on carbon emissions in the province exists in the eastern region and the central region; the spatial spillover effect of FDI in other provinces on carbon emissions in the province exists in the eastern region and the western region, but the eastern region has a positive promoting effect and the western region has a negative inhibiting effect. Therefore, the quality of FDI should be further optimized and governed according to local conditions to achieve the goal of energy conservation and emission reduction, so as to promote the coordinated development of China's economy and environment.

Intensive human activities and resource consumption in China have led to increasing carbon emissions, placing enormous pressure on achieving sustainable development goals. Nonetheless, the effects of population-related factors and carbon emissions remain controversial. This study focuses on the spatiotemporal differences in and influencing effects of per-capita carbon emissions using 2010–2019 panel data covering 30 regions in China. Differing from previous studies, population-related factors are employed to classify the 30 regions into 4 classes, and kernel density estimation, σ convergence and spatial econometric models are used to analyse the spatiotemporal differences in and influencing effects of per-capita carbon emissions. The results demonstrate that overall per-capita carbon emissions rose, but there was heterogeneity in the change in per-capita carbon emissions in the 4 classes of regions. The difference in regional per-capita carbon emissions has been widening, but the change rate of the difference stabilized. Overall, per-capita carbon emissions are heavily affected by household size; however, the driving forces behind per-capita carbon emissions in the 4 classes of regions vary. These results suggest that precise and coordinated governance of carbon emissions and reverting to the traditional household structure should be considered to meet the dual carbon goal.

With carbon emissions becoming more and more closely related to human activities and China being the world's largest emitter of carbon dioxide, the cost of environmental quality deterioration is high and environmental sustainability is low. Against this background, it is urgent to implement low-carbon recycling and green development by introducing green funds based on the role of environmental regulation intensity. Therefore, based on the data of 30 provinces from 2004 to 2019, the paper considers the dynamic impact of environmental regulation intensity and green finance development on regional environmental sustainability. The provincial data involve specific spatial locations and focus on the economic interactions among provinces, and the spatial econometric model is very applicable. Based on the spatial econometric model, this paper empirically examines the direct effect, spatial spillover effect, and total effect, considering both spatial and temporal patterns. The research shows that (1) China's provincial environmental sustainability shows a noticeable spatial agglomeration effect, which is significantly and spatially autocorrelated and clustered. (2) Based on the national level, an increase in the intensity of environmental regulation will significantly increase regional environmental sustainability, and the development of green finance will also significantly increase regional environmental sustainability. Additionally, the environmental regulation intensity has a significant positive spatial spillover effect, which may increase environmental sustainability in neighboring provinces. There is also a significant positive spatial spillover effect of the impact of green finance development on environmental sustainability. (3) Based on the provincial level, the impact of environmental regulation and green financial development on environmental sustainability in each region is significantly positive, with the most significant impact in the western region and the smallest coefficient in the eastern region. The paper proposes policy and managerial implications to promote regional environmental sustainability based on the above findings.

Spatial spillover effect of carbon emission trading policy on carbon emission reduction: Empirical data from transport industry in China

The Paris agreement is a unified arrangement for the global response to climate change and entered into force on 4 November 2016. Its long-term goal is to hold the global average temperature rise well below 2 °C. China is committed to achieving carbon neutrality by 2060 through various measures, one of which is green technology innovation (GTI). This paper aims to analyze the levels of GTI in 30 provinces in mainland China between 2001 and 2019. It uses the spatial econometric models and panel threshold models along with the slack based measure (SBM) and Global Malmquist-Luenberger (GML) index to analyze the spatial spillover and nonlinear effects of GTI on regional carbon emissions. The results show that GTI achieves growth every year, but the innovation efficiency was low. China’s total carbon dioxide emissions were increasing at a marginal rate, but the carbon emission intensity was declining year by year. Carbon emissions were spatially correlated and show significant positive agglomeration characteristics. The spatial spillover of GTI plays an important role in reducing carbon dioxide emissions. In the underdeveloped regions in China, this emission reduction effect was even more significant.