Adaption to climate change risk in eastern China: Carbon emission characteristics and analysis of reduction path

To accurately measure the carbon emission intensity of tourism, a comprehensive measurement method is proposed in this study. This method combines statistical data and standard deviation ellipse analysis, which can reflect the actual situation of carbon emission in tourism more comprehensively. The spatial autocorrelation of regional tourism is obtained by global Moran's I index and local Moran's I index, and the spatial and temporal evolution characteristics of tourism carbon emission intensity are extracted by standard deviation ellipse analysis. By calculating the consumption stripping coefficient, carbon emission intensity and total carbon emission of tourism, the carbon emission intensity of tourism is calculated. China is divided into eastern, central and western regions, and the carbon emission level and intensity in the region are calculated. The results show that: (1) from 2012 to 2021, the carbon emissions of tourism in various regions generally showed an increasing trend, but the carbon emissions in the eastern region were the highest. (2) From 2018 to 2021, the carbon emission intensity of tourism in different regions is basically the same, and the research period shows a certain downward trend. (3) The accuracy of calculating the carbon emission intensity of tourism in each region obtained by this method can reach 86.5%.

The characteristics of common prosperity include harmonious relationships between humans and the environment, as well as sustainable economic and social growth. The process of achieving common prosperity will necessarily have an impact on carbon emissions. In this article, panel statistics collected from 30 Chinese provinces and cities between the years 2006 and 2020 are utilized to assess the level of common prosperity and the intensity of carbon emissions in China. Then the SDM model is applied to explore the effects of the common prosperity level on the intensity of carbon emissions. The findings reveal that: (i) The common prosperity level in China has shown an increasing tendency. Between 2006 and 2020, the mean level of common prosperity increased from 0.254 to 0.486. From the regional perspective, eastern China has seen greater levels of common prosperity than central China, while central China has experienced greater levels of common prosperity than western China; regional disparities in the degree of common prosperity are substantial among Chinese provinces from 2006 to 2020; the common prosperity level is relatively high in economically developed provinces and relatively low in economically backward provinces. (ii) China's carbon emission intensity shows a continuous downward tendency. The annual average intensity of China's carbon emissions decreased from 4.458 in 2006 to 2.234 in 2020. From the regional perspective, the three main regions' carbon emission intensity likewise exhibits a decline in tendency between 2006 and 2020; still, western China continues to have the greatest carbon emission intensity, following central China, while eastern China has the smallest; however, certain provinces, notably Inner Mongolia and Shanxi, continue to have high carbon emission intensity. (iii) China's common prosperity level and carbon emission intensity both exhibit positive spatial autocorrelation at a 1% significant level under the adjacency matrix. The spatial agglomeration effect is significant, and adjacent provinces can affect each other. (iv) The SDM (Spatial Durbin Model) model test with fixed effects finds that the increase in the level of common prosperity suppresses the intensity of carbon emissions in the local area and neighboring regions. (v) The mediating effects model indicates that the process of common prosperity suppresses carbon emission intensity through high-quality economic development, narrowing the income disparity, and the development of a sharing economy.

Global warming caused by greenhouse gas emissions seriously threatens a region’s sustainable environmental and socioeconomic development. Promoting industrial restructuring and strengthening technological innovation have become an important path to achieving pollution and carbon reduction as well as the green transformation of economic structure. This paper explored the mechanism of the mediating effect of technological innovation on industrial restructuring and carbon reduction while accounting for the direct effect of industrial restructuring on carbon emissions. Then, based on China’s provincial panel data from 2001 to 2019, we estimated the carbon emission intensity using the Intergovernmental Panel on Climate Change (IPCC)’s methods and analyzed its spatiotemporal evolution characteristics. Finally, we constructed a fixed-effect model and a mediating effect model to empirically analyze how industrial restructuring and technological innovation affect carbon emission intensity. The results are as follows: (1) From 2001 to 2019, China’s carbon emission intensity showed a continuous downward trend, with a pronounced convergence trend; there were obvious differences in carbon emission intensity between eastern, central, and western regions (western region > central region > eastern region) due to the unbalanced industrial structure. (2) In terms of direct effects, industrial restructuring can significantly reduce carbon emission intensity. The intensity of the effect is inversely proportional to the level of industrial restructuring, and the results of sub-regional tests are similar. Nevertheless, there is an obvious regional difference in the size of the carbon emission reduction effect of industrial restructuring in the east, central, and western regions. (3) In terms of indirect effects, industrial restructuring can reduce carbon emission intensity by enhancing technological innovation, and it acts as a mediating variable in the process of industrial restructuring to reduce carbon emission. Finally, we put forward recommendations for promoting industrial restructuring, strengthening green technological innovation, and properly formulating carbon reduction measures to provide a reference for countries and regions to achieve the goals of carbon neutrality, carbon peaking, and high-quality economic development.

The farming-pastoral ecotone has an important strategic place in the energy supply and ecological layout of China. Thus, exploring the spatial and temporal variation characteristics of carbon emissions in this region will help to deeply understand the information on the historical carbon emissions in China's energy production bases and provide data references for the formulation of differentiated emission reduction policies and the promotion of regional energy-saving and carbon-reducing measures, which is of great significance for the realization of low-carbon economic development. This study constructed a spatialization model of carbon emissions based on land use, night lighting, and provincial energy consumption data； explored the spatiotemporal changes and aggregation characteristics of carbon emissions in the farming-pastoral ecotone from 1995 to 2020 using the global Moran's index and hotspot analysis； and then combined it with the slack-based measure model to calculate the carbon emission efficiency and emission reduction potential of each city from 2010 to 2020 and classify cities to propose a differentiated emission reduction path. The results showed that, firstly, the estimated results at the prefectural city level of the carbon emission spatialization model constructed in this study with multi-source data could reach an R2 of 0.92 for a linear fit. Secondly, the total carbon emissions in the farming-pastoral ecotone increased from 176.29 million tons in 1995 to 1 014.51 million tons in 2020. However, the carbon emission intensity and growth rate both decreased, which was related to adjusting the energy structure and improving energy efficiency. Regarding spatial distribution, the cities with high carbon emissions over time were Datong, Baotou, and Yulin in order. Thirdly, the carbon emissions in the study area showed a significant global spatial positive correlation at the county level, with the hot spots mainly located at the junction of Shanxi, Shaanxi, and Inner Mongolia, while the cold spots were extended from Yanan City to Qingyang and Guyuan City after 2010. Finally, based on the differences in carbon emission efficiency and reduction potential, cities could be classified into four types： "high-efficiency and high potential," "low-efficiency and high potential," "high-efficiency and low potential," and "low-efficiency and low potential" to implement targeted emission reduction strategies.

With the proposal of “Carbon Peak and Carbon Neutrality” goals, China is facing a more serious carbon emissions reduction situation, and how the booming digital economy effectively helps China’s carbon emissions reduction is one of the most urgent things that should be solved. To study the impact of the digital economy on carbon emission intensity, this paper is based on the panel data of 30 provinces in China (excluding Tibet, Hong Kong, Macao, and Taiwan) from 2011 to 2021, and applies the double-fixed effect model and the threshold effect model to study the impact of the digital economy on carbon emission intensity and the mechanism of its action, as well as to analyze the mechanism of the digital economy’s action on carbon emission intensity from the perspective of technological innovation. The results of the study show that: i) The digital economy can reduce the intensity of regional carbon emissions; ii) The carbon emission reduction effect of the digital economy is non-linear, and its carbon emission reduction effect gradually increases with the level of development of the digital economy; iii) In addition to the direct impact of the digital economy on carbon emission intensity, it also has an indirect impact on carbon emissions through technological innovation; iv) There is regional heterogeneity in the carbon emission reduction effect of the digital economy, and the carbon emission reduction effect is more significant in the central and western parts of the country and regions with a high level of human capital development. Based on the conclusions obtained, this paper suggests: i) The rational integration of the digital economy and regional development should be strengthened; ii) Strong provinces in the digital economy should be encouraged to help weaker provinces, to narrow the “digital divide” between provinces; iii) Differentiated development strategies should be formulated in accordance with local conditions, to give full play to the optimal effect of the digital economy in carbon emission reduction.

To accurately calculate the carbon emission of integrated energy system (IES), and fully explore the potential for load side on carbon emission reduction, this paper proposes a method of guiding load to participate in demand response based on node carbon emission intensity, and constructs a bi‐layer low‐carbon scheduling model for source‐load collaborative optimization. First, the carbon flow calculation model of IES in the total process is established, such as source, network, load, and storage. It can depict the carbon emission characteristics of energy conversion equipment and energy storage devices. The principle of proportional sharing is used to track carbon emission flows, the changes in carbon emission intensity at each node is perceived from a spatiotemporal perspective. Second, carbon flow analysis is incorporated into the load demand response mechanism, and a carbon emission model for load aggregator (LA) after demand response is established based on the node carbon emission intensity. Third, a bi‐layer low‐carbon scheduling model is constructed, which considers the source‐load collaborative optimization. The upper layer is the optimal economic dispatch of IES operators, while the lower layer is the optimal economic dispatch of LAs. Finally, the effectiveness of the proposed method is verified using the system as an example, such as improved 14‐node power grid, 6‐node heating network, and 6‐node nature gas network.

Achieving the goals of carbon peak and carbon neutrality is crucial for the balance of global economic development with carbon emissions reduction and ecological environment protection, which are essential for the sustainability of human development. Digital inclusive finance (DIF), as an emerging force capable of promoting economic growth and technological innovation, plays a significant role in curbing carbon emissions. By using the panel data of 30 provinces in China from 2011 to 2021 and employing the panel vector autoregression (PVAR) model, this study empirically investigates the impact of DIF on total carbon emissions (TCE) and carbon emission intensity (CEI) from the perspective of technological innovation. The results show that DIF significantly reduces TCE and CEI and can further decrease TCE and CEI by promoting the level of technological innovation. The results of the impulse response function (IRF) reveal that technological innovation has a more significant and volatile impact on CEI compared to its effect on TCE. Moreover, heterogeneity analysis suggests that the impact of DIF on the reduction in carbon emissions is characterized by regional heterogeneity, with the impact of DIF on TCE in the central regions being the most pronounced, significantly influenced by the spillover effects from the eastern regions. Further research finds that the western regions exhibit a more significant impact of technological innovation levels on CEI compared to the eastern regions, with a discernible trend towards the convergence of inter-provincial disparities in CEI in the process of development.

Economic policy and energy policy are two major factors of energy consumption and carbon emissions in China. This paper analyzed China’s carbon emission intensity from two perspectives: per capita carbon emission of primary energy (CEPE) and final carbon emission intensity (FCEI), that is, final carbon emission per unit of GDP. Based on the latest available China statistics data, Divisia decomposition was applied to decompose the changes of carbon emission intensity. Study results showed that economic policy and energy policy factors in different periods impact on carbon intensity change and contribution rates are different. In terms of per capita CEPE, the level of economic development is major factors of per capita CEPE increase, which is increased by 309% and improved by 4.7 tCO2 during 1980–2012, where economic development promoted to increase per capita CEPE with contribution rate of 215.7%, while energy efficiency and structural changes played a role in reducing carbon emissions per capita. For analysis of FCEI which reduced by 52% during 1996–2012, energy efficiency is the main determinant of reduced emission intensity with the contribution rate of 106%, followed by the structural change of energy, while changes of industrial structure promote carbon emission intensity improvements. Thus, economic policy, energy policy in the different impacts on carbon emissions, and the contribution rate are different, and finally, preliminary recommendation and policy reflection for energy development policy were concluded.

As the key object of carbon emission reduction, resource-based cities’ carbon emission problems are related to the achievement of China’s goals to peak carbon emission and achieve carbon neutrality. In this paper, 115 resource-based cities with abundant natural resources in China were studied, and spatial analysis techniques such as LISA (Local Indicators of Spatial Association) time path and spatial-temporal transition were used to explore their spatial divergence pattern and spatio-temporal evolution characteristics of carbon emission intensity from 2000 to 2019, while geodetector model was used further to reveal their drivers and impacts on the environment. It is found that 1) the carbon emission intensity of resource-based cities shows a significant decreasing trend, with significant differences in carbon emission intensity and its decreasing rate in different development stages and resource-type cities. The overall trend of growing cities, declining cities, mature cities and regenerating cities decreases in order. The carbon emission intensity of cities in the energy, forest industry, general, metal and non-metal categories gradually decrease. The spatial pattern of carbon emission intensity has strong stability, with an overall spatial distribution of high in the north and low in the south. 2) The spatial structure of carbon emission intensity in resource-based cities has strong stability, dependence and integration, with the stability gradually increasing from north to south and the path dependence and locking characteristics of the carbon emission intensity pattern slightly weakened. 3) The spatial divergence of carbon emission intensity in resource-based cities is the result of the action of multiple factors, among which the level of financial investment, urban economic density, urban population density, urban investment intensity and energy use efficiency are the dominant factors. 4) The leading drivers of carbon emission intensity are different in cities at different development stages and with various resources, and grasping the characteristics of carbon emission intensity changes and drivers of various resource-based cities can better provide targeted countermeasures for resource-based cities to achieve carbon emission reduction targets and sustainable development.

Currently, scientifically and reasonably specifying carbon emission reduction measures in the context of "double carbon" has become a common concern worldwide. China's administrative divisions have a notable impact on the formulation and implementation of relevant policies. Therefore the carbon emissions must be calculated accurately under China's administrative divisions at different scales. The spatiotemporal change characteristics of absorption and carbon emissions can provide scientific basis for the formulation of reasonable and differentiated carbon emission reduction policies in different administrative regions in China. To this end, this study used multi-source data such as remote sensing and statistics and integrated ecological models, statistics, and GIS space analysis and other methods to analyze the spatiotemporal dynamic change characteristics of carbon emissions and carbon absorption at different administrative scales （provinces, cities, and counties） in China. The results showed that： ① The total carbon absorption of vegetation in China continued to increase from 2000 to 2021 and the average value gradually increased. Differences were observed in spatiotemporal changes in carbon emissions at different administrative scales. The spatiotemporal changes at smaller scales were more evident. Carbon emissions showed obvious spatial differences of "high in the north and low in the south, high in the east and low in the west." ② The spatiotemporal distribution of CPI at the administrative scale was similar to that of carbon emissions and the overall trend was increasing annually. The pressure of carbon emissions on carbon absorption gradually weakened from the east to the central and western regions. ③ Spatiotemporal hotspot analysis showed that the overall spatial distribution of cold and hot spots in China's carbon absorption was as follows： In the spatial pattern of "hot in the east and cold in the west," the spatial distribution of cold and hot spots of carbon emissions showed agglomeration characteristics. The provincial scale was primarily oscillating hotspot whereas municipal and county scales were majorly continuous hot spots. Further results revealed that： ① Carbon absorption in different regions and periods in China showed significant variability, especially in the central and eastern regions. The possibility of offsetting carbon emissions by increasing carbon absorption remains. ② At the same scale, administrative regions （such as different provinces） and lower-level administrative regions at another scale （such as different cities in the same province） showed varying degrees of variability in carbon absorption and carbon emissions. Therefore, taking provincial administrative regions as an example for subsequent formulation considering carbon trading, emission reduction, and other policies, we should first consider the coordination of emissions between different cities in the province and then consider the coordination between provinces, which is expected to better promote the implementation of relevant policies.

Under the background of carbon peak and carbon neutralization, it is vital to study the impact of digital economy on carbon emission reduction. Based on a provincial panel data from 2013 to 2019, this paper establishes a dynamic panel model, a dynamic spatial autoregressive model, and a dynamic threshold model to study the impact of digital economy on carbon emission intensity. Our findings show that digital economy has a significant inhibitory effect on carbon emission intensity. Results of regional heterogeneity show that the central region can transform the impact of digital economy on carbon emission reduction more efficiently. After adding the time lag term of carbon emission intensity, the impact coefficient of digital economy is still significant. Carbon emission intensity has obvious spatial effect, and the carbon emission of adjacent areas will significantly inhibit local carbon emission reduction activities. Under the threshold of innovation and environmental regulation, the emission reduction effect of digital economy is different. For regions with low technological level, digital economy is difficult to give full scope to its emission reduction advantages. At the same time, stricter environmental regulations can cooperate with digital economy to accelerate regional carbon emission reduction. Therefore, China should continue to improve the construction of digital infrastructure and promote the reform and innovation of enterprise digital technology in order to release the carbon emission reduction effect of digital economy.

Spatial effects and heterogeneity analysis of the impact of environmental taxes on carbon emissions in China

As a major carbon emitter, the electricity sector is crucial to the realization of China's emission reduction objectives. Existing studies focus mostly on the influencing factors, emission efficiency and low carbon development of carbon emissions in the electricity sector. Missing from the literature is an analysis of spatial characteristics of carbon emissions and the embodied carbon emission transfer caused by the separation of electricity production and consumption, which is the basis for assigning the responsibility for emission reduction. Thirty provinces in China were taken as research objects, and Moran's I index was adopted to analyze the spatial characteristics of the electricity sector's carbon emissions and carbon emission intensity. Based on multiregional input-output tables, we compared the transfer situation of China's provincial electricity carbon emissions in 2010 and 2015. The results demonstrate that, from 2010 to 2015, the electricity carbon emissions in 20 provinces increased, whereas the carbon emission intensity in 21 provinces decreased. Carbon emissions and carbon emission intensity of electricity in most provinces demonstrate positive spatial clustering characteristics. The total amount of carbon emission transfer in the electricity sector increased from 421.22 million tons in 2010 to 581.369 million tons in 2015, the number of net transfers out of areas increased from 13 to 15, and the number of net transfers into areas decreased from 16 to 15. The active degree of carbon emission transfer reveals the eastern region > the central region > the western region. Different emission reduction policies should be formulated based on the difference in resource endowment between the north and south. Provinces that transferred out large amounts of electricity carbon emissions should take greater responsibility for emission reduction. Integr Environ Assess Manag 2022;18:258-273. © 2021 SETAC.

The new quality productivity, which integrates the concepts of technological innovation, industrial upgrading, and green development, plays a pivotal role in achieving carbon emission reduction targets. Given that current research on the relationship between new quality productivity and carbon emission intensity remains limited, in order to delve into the impact of new quality productivity on carbon emission intensity, its underlying mechanisms, as well as its heterogeneous performance across different regions and city types, we select panel data from 251 Chinese cities spanning from 2010 to 2021 and conduct an empirical analysis using a panel data two-way fixed-effects model. The research findings reveal that new quality productivity can significantly reduce carbon emission intensity. Further analysis demonstrates that new quality productivity can achieve a reduction in carbon emission intensity by enhancing urban innovation levels and the intensity of government environmental regulations. Moreover, the heterogeneity analysis indicates that, compared with other regions, the inhibitory effect of new quality productivity on carbon emission intensity is more pronounced in the western regions and non-resource-based city samples. This study not only enriches the relevant theories on the relationship between new quality productivity and carbon emissions but also provides a crucial basis for governments to formulate targeted carbon emission reduction policies. Based on this, this paper proposes that investment in areas related to new quality productivity should be increased, technological innovation and industrial upgrading should be promoted, and government environmental regulations should be strengthened. Particular attention should be paid to the development of the western regions and non-resource-based cities to give full play to the role of new quality productivity in carbon emission reduction.

Predictions of carbon emission intensity based on factor analysis and an improved extreme learning machine from the perspective of carbon emission efficiency