CO2 Emissions In China Research Articles

Geographic matching analysis between green finance development and carbon emissions in China’s new era of environmental transition

Green finance plays a pivotal role in driving sustainable development. While its effectiveness in promoting green development is well-established, the impact of regional heterogeneity on its outcomes warrants further investigation. By introducing the Spatial Durbin Model (SDM) model, we analyze the causal relationship between provincial carbon emissions and green finance development in China, accounting for spatial spillovers across provinces. A geographic mismatch between regions of green finance development and carbon emission concentration is revealed. Central and Western areas exhibit higher carbon emissions, indicating a greater need for targeted green finance initiatives to promote sustainable development. However, green finance growth in these areas remains sluggish. Moreover, carbon emissions hinder local green finance development and negatively impact neighboring regions. We present new insights into the influence of carbon emissions on green finance, highlighting the importance of inter-regional collaboration and the potential applicability to other countries facing similar challenges.

Development of Ecological Low-Carbon Agriculture with Chinese Characteristics in the New Era: Features, Practical Issues, and Pathways

Agriculture is not only the source of carbon emissions, but also an important carbon sink. The development of low-carbon agriculture in China is closely related to achieving the national strategic goal of “dual carbon.” Reducing carbon emissions in China’s agricultural sector and accelerating the development of ecological low-carbon agriculture (ELA) are extremely important and urgent, both from the perspective of the long-term common welfare for mankind and from the perspective of maintaining the sustainable development of agriculture itself. However, ELA is still an emerging concept in China, and its connotation and characteristics are not yet clear. There is a lack of effective paths for orderly and scientific promotion of the development of ELA in China. Based on this, this paper focuses on the emerging concept of ELA and examines ELA with Chinese characteristics from the dimensions of connotation, features, practical issues, and pathways, aiming to provide guidance for the sustainable development of ELA in China. The research results indicate that there are regional development imbalances, low willingness of farmers to participate, insufficient technological reserves, and difficulties in realizing the value of ecological products in China’s ELA. It is necessary to improve the modern agricultural production and operation system, tap into the potential of agricultural emission reduction and carbon sequestration, build a technical system to support the development of ELA, and establish a sound mechanism for realizing the value of ecological products. By continuously improving the regional organizational capacity and performance of ELA, accelerating the advancement of ELA technology and demonstrating its regional promotion, and by establishing and continuously improving the support system for ELA, one can promote the sustainable development of ELA in China. In addition, the research findings of this paper can also provide reference for the ecological low-carbon development of the global agricultural sector, supporting the contribution of the agricultural sector to achieving high-quality global sustainable development goals.

Spatiotemporal Differentiation of Carbon Emissions from Logistics Industry at Provincial Scale in China Under the Background of High-quality Economic Development

Since 2010, the Chinese economy has transitioned from a high-speed growth model to a high-quality development model. During this period, the logistics industry has witnessed rapid growth, leading to significant carbon emissions and posing severe threats to the ecological environment. To investigate the spatiotemporal variations in carbon emissions in China's logistics industry, we conducted a correlation analysis using Moran's I index and a bivariate spatial autocorrelation model from 2010 to 2021. Additionally, we employed a geographically and temporally weighted regression model （GTWR） to examine the spatial heterogeneity of factors influencing provincial-level logistics-related carbon emissions. The results indicated that over the study period, there was a shift from insignificant spatial relationships to significant positive spatial correlations among provincial-level logistics carbon emissions in China. Furthermore, varying degrees of spatial clustering were observed. The findings regarding factor heterogeneity revealed that freight turnover volume, per capita GDP of the logistics industry, and infrastructure level exhibited positive spatial correlations with logistics-related carbon emissions, whereas energy intensity showed negative spatial correlations with such emissions. Comparing the results from the geographically weighted regression （GWR） and ordinary least squares regression （OLS）, it was evident that the adjusted R-squared values for the OLS, GWR, and GTWR models were 0.541, 0.567, and 0.838, respectively. This suggests that our adopted GTWR model provided a superior fit and offered better explanations for spatiotemporal heterogeneity between various influencing factors and logistics-related carbon emissions. These research findings can serve as valuable references for formulating province-specific strategies to reduce carbon emissions within China's economy under its high-quality development context.

Spatiotemporal Characteristics and Influencing Factors of the Synergistic Effect of Pollution Reduction and Carbon Reduction in China

Based on the use of the coupling coordination model to calculate the coupling coordination degree of carbon emission and pollutant control, the national, regional, and provincial spatiotemporal characteristics of the synergistic effect of pollution control and carbon emissions reduction in China were further analyzed, facilitating the crucial to identification of key areas. The fixed effects regression models and provincial panel data from 2006 to 2020 were used to explore factors contributing to better synergizing the reduction of pollution and carbon emissions in China. On this basis, the adjustment variable of R&D investment intensity was introduced, and the regulation effect model was constructed to further explore the influence mechanism of the synergistic effect of pollution reduction and carbon reduction. The results showed that： synergy exists between carbon emission reduction and the air pollution control system, the evolution of the synergistic effect of pollution reduction and carbon reduction in China presented an inverted "U"-shaped trend from 2006 to 2020, and there was spatial aggregation and a spatial spillover effect in pollution reduction and carbon reduction. The synergistic governance of carbon emission and pollutant control was still at a relatively low level. The carbon emission and air pollutant emission systems were still in an unstable and uncoordinated state. The results showed that： The degree of coordination of eastern China, central China, and western China decreased in turn. At the national level, energy consumption structure, per capita GDP, and the proportion of green investment were the main factors affecting the synergistic effect of pollution reduction and carbon. The heterogeneity of the influencing factors existed in the central, eastern, and western regions on industrial structure, energy consumption structure, energy utilization efficiency, per capita GDP, urbanization rate, the proportion of green investment, and transportation structure. The intensity of R&D played a significant moderating effect in the whole country, eastern, and central regions. However, no significant moderating effect was identified in the western region. In the eastern region, the urbanization rate, the proportion of green investment, and the transportation structure could not have a significant effect on the synergistic effect of pollution reduction and carbon reduction alone, and it must be coordinated with the intensity of R&D.

Land use policy implications of demographic shifts: Analyzing the impact of aging rural populations on agricultural carbon emissions in China

China is facing a unique demographic challenge with its population entering a phase of negative growth, alongside the growing issue of Rural Population Aging (RPA). This has brought significant attention to the broader population challenges the country is encountering. While the aging of rural populations has been a focal point of discussion, there remains a need for deeper insights into how it specifically impacts Agricultural Carbon Emissions (ACE). To address this knowledge gap, this study has devised a research framework grounded in the human-land relationships theory. This framework seeks to analyze the influence of RPA on ACE. Empirical tests have been carried out using provincial (municipal) panel data spanning from 2002 to 2019. The study's findings indicate that RPA exacerbates ACE, particularly in regions with a pronounced aging rural demographic. However, this adverse effect is lessened in coastal areas and placed with a lighter rural aging phenomenon. Additionally, the negative impact gradually diminishes in the long term. From a land utilization perspective, RPA contributes to Land Misallocation (LM), serving as a critical conduit through which RPA influences ACE. Nevertheless, mitigating factors such as Land Transfer (LT) and Land Scale (LS) help alleviate this negative influence. This mitigation is more pronounced in the long run. In the context of smallholder farmers, the study suggests that a moderate-scale expansion and the promotion of new agricultural models can be instrumental in achieving broader agricultural carbon reduction targets.

A Study on the Impact of China’s Prefabricated Building Policy on the Carbon Reduction Benefits of China’s Construction Industry Based on a Difference-in-Differences Method

The construction industry is a significant contributor to carbon emissions in China. To effectively meet the “dual carbon” targets, several provincial regions within the country started to implement policies promoting prefabricated buildings. This study examines data from 18 provinces in China over the period from 2012 to 2021, treating the introduction of prefabricated building policies as a quasi-natural experiment. Utilizing the difference-in-differences methodology, this research assesses the impact of these policies on the carbon emission performance of China’s construction sector and evaluates the robustness of the findings. The results indicate that the prefabricated building policies positively influenced the carbon emission efficiency of the construction industry. Specifically, these policies enhance carbon emission efficiency by increasing labor productivity, optimizing the allocation of mechanical resources, and improving the utilization rate of building materials. Additionally, the effectiveness of these policies is positively correlated with the level of regional technological innovation, environmental protection efforts, and the advancement of energy structure optimization. The study concludes with several policy recommendations aimed at further enhancing the effectiveness of prefabricated building policies.

Research and analysis of urban-rural residential carbon emissions in China

Residential carbon emissions are an important component of anthropogenic carbon emissions. a significant increase in residential carbon emissions has become a reality under the global urbanization process. In this context, this paper built a feature combination value model based on NPP-VIIRS nighttime light remote sensing data, and divided urban-rural areas through breakpoint analysis method and reference comparison method. Then, explored the characteristics and differences of residential carbon emissions and per capita residential carbon emissions in nine different levels of cities in 2019 from the perspective of urban-rural areas. The results indicate that the residential carbon emissions and per capita residential carbon emissions shows the spatial distribution characteristics of first tier cities>second tier cities>third tier cities. Among them, the residential carbon emissions in Beijing, Guangzhou, Nanjing, Taiyuan show a distribution pattern of urban>urban-rural fringe>rural. The residential carbon emissions in Shijiazhuang, Wuxi, Xiangyang, Zunyi, Huai’an show a distribution pattern of rural>urban-rural fringe>urban. The per capita residential carbon emissions of urban areas are relatively low, while the per capita residential carbon emissions of rural areas are relatively high, show a distribution pattern of rural>urban-rural fringe >urban. The results can help the Chinese government balance the needs of urban-rural development in different levels of cities, so as to formulate targeted carbon emission reduction policies and achieve low-carbon goals.

What lessons and experiences can be learned from China for practicing low carbon city: A policy instrument perspective

Practicing low carbon city (LCC) is the key strategy for reducing carbon emissions in China. Policy instruments are important tools to guide LCC practice. Most existing studies failed to demonstrate the performance of LCC policy instruments in China. This study analyzes the lessons and experiences of Chinese cities for implementing LCC from the policy instrument perspective. This study developed a comprehensive two-dimensional analytical framework to classify policy instruments for LCC based on considerations of both diversity and abundance. Empirical analyses were conducted using data collected from 35 sample cities in China spanning the period from 2006 to 2020. The empirical findings reveal significant heterogeneity in the implementation of LCC policies across Chinese cities with varying levels of economic development. This research draws a holistic picture of LCC policy instruments in China, which can provide important references for governors to formulate targeted low-carbon policies.

How does enterprise digitalization affect corporate carbon emission in China: A firm-level study

This paper develops a theoretical framework to examine the influence of enterprise digitalization on corporate carbon emissions. Then it empirically measures the extent of digitalization in Chinese enterprises through their share of intermediate inputs to their value added from information and communication technologies. Data from the China Enterprise Tax Surveys and Inter-Country Input-Output Tables from 2007 to 2016 are used for the empirical analysis. It is noted that digitalization in enterprise manufacturing leads to an increase in corporate carbon emissions (CCE). The impact from the service sector, however, is not significant. The exacerbation of CCE is observed primarily among low-leverage enterprises, downstream industries, and firms located in relatively lower-income regions. In accordance with the theoretical model, it is observed that enterprise digitalization intensifies CCE by crowding out investment in environmentally friendly green production and by altering production patterns towards mass production. However, an efficient market mechanism effectively mitigates this exacerbation effect. The findings reveal the misalignment of enterprise digitalization transformation modes with long-term “low carbon” objectives, thereby exposing the need for appropriate policy interventions.

Balancing progress and preservation: analyzing the role of technological innovation in mitigating environmental degradation caused by energy consumption in China

China is undergoing a significant transition as it modernizes its energy consumption system and changes its approach to economic development. However, this transformation comes at the expense of environmental quality. The utilization of technological innovation has emerged as a significant catalyst for China's pursuit of environmentally conscious and sustainable development. Accordingly, this study utilizes panel data from 30 Chinese provinces spanning the years 1997–2021. The research employs the MMQR and BSQR to conduct an empirical analysis to find the influence of energy consumption (EC) and technological innovation (TI) on environmental degradation (ED). The findings of the research indicate that EC is a prominent manifestation of ED, while TI contributes negatively to ED. The inclusion of an interaction term suggests that the relationship between TI and ED can be influenced by the extent to which energy conservation and emission reduction are improved. The study's conclusion revealed that economic growth, population, and industrial structure are positively associated with CO2 emissions in China. This study offers a set of recommendations for policymakers to formulate policies related to environmental quality.

Urban transport emission prediction analysis through machine learning and deep learning techniques

About 6.6 million people die every year from air pollution diseases globally. Transportation industry is considered one of the leading contributors in air pollution. This research utilizes deep learning and machine learning techniques to predict China’s transport-related CO2 emissions and energy needs by utilizing variables like population, car kilometers, year and GDP per capita. The outcomes have been analyzed using six analytical measures: determination coefficient, RMSE, relative RMSE, mean absolute percentage error, mean bias error and mean absolute bias error. Findings indicate that yearly increase in transport-related CO2 emissions in China will be 3.66%, and transport energy consumption will increase by 3.8%. Energy consumption and transport CO2 emissions are projected to rise by roughly 3.5 times by 2050 as compared to current levels. Therefore, government should re-evaluate its energy investment plans for the future and institute new rules, and standards regarding transport-related energy consumption and pollution reduction.

The spatiotemporal heterogenous impact of urban multidimensional compactness on household carbon emissions in China

The spatiotemporal heterogenous impact of urban multidimensional compactness on household carbon emissions in China

Impact of energy consumption patterns on peak emissions in China's carbon neutralisation process

The investigation of the impact of energy consumption patterns on peak emissions in China is pertinent and crucial for achieving global climate goals. The study aims to form a forecast of carbon emissions in China and diagnose the achievement of established decarbonization goals based on structural changes in primary energy consumption until 2030. To achieve the goal, the study formed a Dynamic stochastic general equilibrium (DSGE) model with the implementation of Markov chains. The obtained research results yield important insights into the influence of various scenarios on carbon emissions in China by the year 2030. Specifically, considering structural transformations, an increase in emissions by 15 % can be anticipated, which is 7 % less than predicted by the classical scenario. Among the proposed scenarios, the Net Zero scenario is deemed the most efficient, where emissions could reach a level of 8509.5 million tons of CO₂. Conversely, the New Momentum scenario proves to be the most precarious, as it entails an escalation in coal usage, leading to a 4 % increase in emissions, amounting to 13264.84 million tons of CO₂. The contribution of this study is the approach to conducting carbon emission projections, which is based on the DSGE model with the implementation of Markov chains. This makes it possible to assess the adequacy of the set targets for carbon reduction based on alternative scenarios, which in crisis conditions can overlap and thus complement each other.

Analysis of the Characteristics and Driving Mechanisms of Carbon Emission Decoupling in the Hu-Bao-O-Yu City Cluster under the “Double Carbon” Target

The Hu-Bao-O-Yu urban area is a major source of carbon emissions in China. It is also a major source of energy exports and high-end chemicals in China. Reaching peak carbon emissions early is especially important for meeting the national peak goal. For urban areas that rely on natural resources, we need to make it clearer how carbon emissions and economic growth affect each other and slowly break the strong link between the two. Therefore, in this paper, based on the data on carbon emissions, the decoupling state and the driving mechanism of carbon emissions in the Hu-Bao-O-Yu City group are researched by using the Tapio decoupling model and GDIM method. A new decoupling index model is constructed by combining GDIM and the traditional decoupling model. The main findings are as follows: (1) The Hu-Bao-O-Yu urban agglomeration, Ordos City, Baotou City and Yulin City have significant growth trends in annual carbon emissions, with Yulin City being the most important carbon source for the Hu-Bao-O-Yu urban agglomeration and its economic contribution to carbon emissions of the whole urban agglomeration is the most efficient. (2) The decoupling of Hu-Bao-O-Yu, Huhhot City, Baotou City, and Ordos City is dominated by expansionary negative decoupling, whereas Yulin City has strong negative decoupling. (3) The Hu-Bao-O-Yu urban cluster mainly affects the carbon intensity of fixed asset investments and output carbon intensity, which is a key part of the carbon separation process. The energy scale and structure also play a part in this process over time. (4) Changes in GDP per capita are a bigger part of changes in carbon emissions in the Hu-Bao-O-Yu urban agglomeration. These changes also determine the direction for changes in carbon emissions in the Hu-Bao-O-Yu urban agglomeration. In the future, the Hu-Bao-O-Yu urban agglomeration needs to coordinate its economic growth. Ordos and Yulin need to speed up the optimisation and transformation of their energy structures. Baotou needs to push for the low-carbon transformation of its industries. Huhhot needs to do more research on carbon sequestration technology and spend more on environmental protection. This will make the Hu-Bao-O-Yu urban agglomeration a resource-saving urban agglomeration and improve its ability to reduce emissions.

Towards sustainable development: Can green digital finance become an accelerator for reducing pollution and carbon emissions in China?

As a fusion of financial ''digitalization'' and ''greening'', green digital finance is crucial for ecological quality enhancement. Based on panel data from 285 cities in China spanning 2011 to 2021, this study aims to examine the synergistic impact of Green Digital Finance (GDFO) on Pollution and Carbon Reduction (PCR). The two-way fixed effect model, moderated effect model, and threshold model are employed to study the direct, transmission, and non-linear effects of GDFO on PCR. Results indicate that GDFO significantly enhances PCR in urban areas, and this conclusion remains valid after several robustness tests. Mechanically, encouraging green technology innovation, promoting industrial structure upgrading, and enhancing financial supervision are effective channels for strengthening the impact of GDFO on PCR. At varying levels of green digital finance and financial supervision, the marginal impact of GDFO on PCR exhibits double threshold characteristics in the form of "U-shaped" and "inverted U-shaped". Furthermore, the impact of GDFO on PCR is more pronounced in the eastern and western regions, resource cities, and cities with a high level of financial development, excellent digital infrastructure, and robust environmental regulation. This study offers theoretical support and empirical evidence for deepening the GDFO to promote PCR at the city level.

Analysis of the effect of digital financial inclusion on agricultural carbon emissions in China

<p style="text-align:justify"><font _mstmutation="1">In the context of China's "dual carbon" goal, digital financial inclusion plays an important role in agricultural carbon reduction. Based on the provincial panel data of 31 provinces in China from 2012 to 2022, this paper analyzes the impact of digital financial inclusion on agricultural carbon emissions and its transmission mechanism. The results show that digital financial inclusion has a significant inhibitory effect on agricultural carbon emissions. From the perspective of mechanism, inclusive digital finance can reduce agricultural carbon emissions through the upgrading of agricultural industrial structure. In addition, in the mechanism of digital inclusive finance affecting agricultural carbon emissions, there is a threshold effect, and only when rural human capital is higher than a certain threshold, digital inclusive finance can show an inhibitory effect on agricultural carbon emissions. Therefore, it is necessary to strengthen the multi-dimensional development of digital technology facilities, the development of digital inclusive finance and the upgrading of agricultural industrial structure, promote the green and low-carbon transformation of agriculture, comprehensively reduce agricultural carbon emissions, and realize the low-carbon and sustainable development of China's agriculture.</font></p>

Prediction and Analysis of Carbon Emissions in China Based on ARIMA-BP Model

Prediction and Analysis of Carbon Emissions in China Based on ARIMA-BP Model

Research into the Correlation between Carbon Emissions, Foreign Energy Investment, and China’s Financial Advancement

Carbon dioxide emissions are the primary driver of global climate change. This study aims to analyze the relationship between inward foreign direct investment in the energy sector and CO2 emissions in China versus other countries. For this, the co-integration methods were used. The results suggested that China should encourage the adoption of green technologies in order to reduce CO2 emissions and enforce strict environmental laws. Another necessary step is to stop the licensing of polluting industries that emit high amounts of CO2 emissions. The present findings can be used to develop state programs for environmental protection. Future research can examine the relationship of FDI in the energy sector with indicators other than pollution with CO2 emissions, for example, with the consumption of renewable energy sources.

Analysis of the Decoupling between Urban Economic Development and Transportation Carbon Emissions in China: Empirical Evidence from 284 Cities

Analysis of the Decoupling between Urban Economic Development and Transportation Carbon Emissions in China: Empirical Evidence from 284 Cities

Impact of globalization and energy consumption on CO2 emissions in China: Implications for energy transition

Despite the importance of understanding the impact of economic globalization on CO2 emissions through renewable and non-renewable energy consumption, previous studies have yielded contradictory results. This study addresses this gap by examining the relationship between trade openness, foreign direct investment, renewable energy consumption, non-renewable energy consumption, and CO2 emissions in China using wavelet analysis. The methodology allows for simultaneous evaluation of the commonality between variables in both time and frequency domains. The results reveal that the causality between variables varies with frequency, with notable reversals in lead-lag relationships. Economic globalization affects CO2 emissions differently through renewable and non-renewable energy consumption, with distinct short-term and long-term dynamics. The findings highlight the need for credible energy transition programs, increased renewable energy utilization, and strengthened global cooperation to balance economic globalization and environmental quality in China. Future research could explore heterogeneity among provinces, nonlinear and asymmetric effects, and expand the scope to other developing economies.