Changes In Carbon Emissions Research Articles

Development of a framework to forecast the urban residential building CO2 emission trend and reduction potential to 2060: A case study of Jiangxi province, China

Investigating the CO2 abatement potential of urban residential building from systematic perspective is essential to reach the urban carbon neutrality target. However, previous studies on building CO2 emission trend forecasting were mainly focused on the building operational phase. In this study, a new framework that includes four building stages under a system dynamic model is developed to simulate urban residential building carbon emission changes and the related reduction potentials under three scenarios in Jiangxi Province up to 2060. Results showed that the overall process carbon emission dynamic had already peaked in 2014 under the three scenarios, with a peak value of 38.52 Mt. It then fell to 9.56 Mt in 2060 under the baseline (BAU) scenario. More importantly, seven carbon abatement measures were adopted during four building activities in this study, and the total carbon reduction was not the sum of the carbon reduction potential of the individual measures. Some carbon abatement strategies displayed synergistic effects such as low-carbon electrification where the combination of electrification and clean energy power generation was the largest contributor to reduced carbon emissions during building operation as a comprehensive carbon reduction measure. By contrast, extending a building's lifetime restrained the carbon abatement potential during the demolition stage, and it inhibited the carbon emission reduction by 24.84 Mt. These results highlight the significant need for effective policy interventions for clean production and the need to improve prefabricated building proportions, promote electrification, improve energy efficiency, strengthen recycling practices, and extend building lifetimes to promote decarbonization of urban residential building system development.

Did electrification of the building sector achieve carbon mitigation? A provincial retrospection in China

Electrification of end-use energy consuming sectors is becoming a widespread choice to combat climate change, yet the decarbonization of electricity determines the quality of carbon emissions reductions from electrification. This study evaluates the changes in carbon emissions from building operations due to electrification in China's 30 provinces over the period 2004–2020. Considering electricity decarbonization, electricity consumption, population and floor area, an assessment framework for carbon emissions reductions from building electrification has been built by Kaya-LMDI method. The results show that (1) The promotion of building electrification causes an increase of approximately 46.88 MtCO2 over the period 2004–2020. The urban residential buildings contribute the largest share, about 54.78%; (2) Almost half of the provinces have failed to achieve carbon emissions reductions with promotion of building electrification; (3) The change in carbon emissions caused by the promotion of building electrification shows a clustering effect in space, with the northern region characterized by high-high clusters and the southern region by low-low clusters. In addition, this study reviews the strategy for power decarbonization and building electrification in China. The overall strategy for building electrification is to accelerate the proportion of electricity consumption in existing buildings and to promote the electrification of new public buildings across the board. Overall, this study provides a reference for other economies and end-use energy sectors to evaluate electrification for carbon emissions reductions, and provides insights and strategies for policy makers on the promotion of building electrification.

Assessing the potential of compaction techniques in tropical peatlands for effective carbon reduction and climate change mitigation

There is a pressing need to tackle carbon emissions from oil palm plantations on tropical peatland, which has garnered significant discussion and concern in recent years. In response, compaction techniques were introduced in Malaysia with the aim of mitigating CO2 emissions by improving moisture levels and reducing soil aeration. This research investigates the impact of mechanical compaction on two distinct ecosystems: a peat swamp forest (PSF) and an oil palm plantation (OPP), characterized by their unique physicochemical properties Using a specially designed compaction apparatus, significant changes in carbon emissions were observed in PSF but not in OPP, with means 1263 and 404 mg CO2-eq m−2 h−1, respectively. This disparity can be due to substrate availability between the two ecosystems. Subsequently, in the PSF, a promising pattern of a percentage ratio of approximately 1:3.5 was observed, indicating a substantial reduction in CO2 emissions (from 1295 to 468 mg m−2 h−1; 64%) alongside a corresponding increase in CH4 emissions (from −50 to 60 µg m−2 h−1; 221%). This finding suggests that compaction alters the aerobic peat horizon, bringing the peat surface closer to the groundwater level. The study underscores the importance of considering confounding factors such as decomposition degree and groundwater fluctuation when assessing the effects of compaction on tropical peat. By shedding light on these complexities, the findings contribute to a better understanding of the efficacy of compaction techniques in reducing emissions of these special case atmospheric pollutants.

Spatiotemporal differences in and influencing effects of per-capita carbon emissions in China based on population-related factors

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.

Factors Driving China's Carbon Emissions after the COVID-19 Outbreak.

The outbreak of the coronavirus disease 2019 (COVID-19) may exert profound impacts on China's carbon emissions via structural changes. Due to a lack of data, previous studies have focused on quantifying the changes in carbon emissions but have failed to identify structural changes in the determinants of carbon emissions. Here, we use China's latest input-output table and apply structural decomposition analyses to understand the dynamic changes in the determinants of carbon emissions from 2012 to 2020, specifically the impact of COVID-19 on carbon emissions. We find that final demand per capita contributed to emissions growth at a slower pace, but production structure drove a greater carbon emissions increase than before the pandemic. Export-led emissions growth rebounded, and investment-led emissions were more concentrated in the construction sector. The carbon intensity of several heavy industries increased, e.g., the nonmetallic products sector, the metal products sector, and the petroleum, coking, and nuclear fuel sector. In addition, lower production efficiency and increased reliance on carbon-intensive inputs indicated a deterioration in production structure. For policy implications, efforts should be undertaken to increase investment in low-carbon industries and increase the proportion of consumption in GDP to shift investment-led growth to consumption-led growth for an inclusive and green recovery from the pandemic.

Decomposition and decoupling analysis of carbon emissions in the Yellow River Basin: evidence from urban agglomerations.

A comprehensive understanding of carbon emission reduction and decoupling in urban agglomerations of the Yellow River Basin (YRB) has significant theoretical and practical value for formulating precise carbon reduction policies and achieving ecological conservation and high-quality development in the region. This study utilized a generalized Divisia index decomposition model to identify the primary driving factors behind carbon emission changes in urban agglomerations of the YRB. Based on this, a model measuring decoupling efforts was constructed to systematically investigate the decoupling relationship between carbon emissions. The research findings indicate that technological progress and output scale are two primary drivers of carbon emission increases in the YRB and its urban agglomerations, whereas technological carbon intensity, output carbon intensity, and energy carbon intensity play key roles in reducing carbon emissions. Except for a few years, the YRB and Jiziwan metropolitan area (JWMA) did not exhibit decoupling effects on carbon emissions. The Shandong Peninsula Urban Agglomeration (SPUA) and Central Plains Urban Agglomeration (CPUA) showed strong decoupling effects from 2016 to 2019. The Guanzhong Plain Urban Agglomeration (GPUA) demonstrated a strong decoupling effect from 2013 to 2019 (except from 2016 to 2017). The Lanxi Urban Agglomeration (LXUA) exhibited a strong decoupling effect from 2014 to 2019. Technological carbon intensity plays a decisive role in the transition from non-decoupling to decoupling. Therefore, the government must increase investments in green and low-carbon technologies and strictly implement carbon reduction measures. Thus, the YRB and its urban agglomerations have considerable potential for carbon emission reduction and strong decoupling effects.

Contribution of multi-objective land use optimization to carbon neutrality: A case study of Northwest China

Land use and land cover (LULC) patterns fundamentally influence the carbon cycle of terrestrial ecosystems, and rational optimization of LULC patterns can contribute to achieving carbon neutrality goals. This study constructs a multi-objective LULC optimization coupled model by integrating the objective of carbon neutrality into the LULC optimization process, with Northwest China serving as the study case. This model integrated the Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) model with the Intergovernmental Panel on Climate Change (IPCC) inventory methodology to assess the spatiotemporal variations in carbon storage and emissions in Northwest China between 2000 and 2020, which aimed to elucidate the mechanisms through which changes in LULC affect carbon neutrality. Additionally, through the amalgamation of the Non-dominated Sorting Genetic Algorithm-II (NSGA-II) and the Patch-Generating Land Use Simulation (PLUS) model, optimized the quantitative structure and spatial layout of four LULC scenarios for Northwest China in 2060: natural development (ND), low carbon emissions (CE), high carbon storage (CS), and carbon neutrality (CN), projected the evolving pathways of carbon storage and emissions within these scenarios while examining the values of different LULC scenarios and their respective contributions to carbon neutrality. The study's findings underscore that the substantial expansion of built-up land development areas and farmland from 2000 to 2020 has resulted in notable carbon storage and emissions increases. Moreover, changes in carbon emissions and carbon storage show significant spatial consistency with changes in LULC patterns. In contrast to the ND scenario, the LULC patterns within the CE, CS, and CN scenarios exhibit higher LULC values and contribute more substantially to carbon neutrality. Remarkably, among these scenarios, the CN scenario stands out for achieving the maximum contribution to carbon neutrality and LULC value, thereby establishing it as the preeminent LULC pattern for the Northwest China region. As the study concludes, it recommended that future developmental strategies in Northwest China prioritize curtailing the expansion of built-up areas while bolstering the conservation of vital ecological zones such as forests and grasslands. This study addresses the inherent tension between regional economic progress and environmental preservation, offering a foundation of scientific insights for effectively managing LULC patterns and attaining carbon neutrality goals.

Spatiotemporal Pattern and Driving Factors of Carbon Emissions in Guangxi Based on Geographic Detectors

Analysis of the spatiotemporal distribution pattern and driving factors of carbon emissions has been a focus of research in recent years. However, at the county level, analyses of the driving factors of carbon emissions are still relatively few. This study selected the Guangxi Zhuang Autonomous Region as the research subject, selecting the normalized difference vegetation index (NDVI), nighttime light index (NLI), gross domestic product (GDP), and population density (POP) as driving factors. Based on the geographic detector method, the spatiotemporal distribution pattern and driving factors of carbon emissions at the county level in Guangxi were investigated. The results show the following: (1) There are significant regional differences in the degree of change in carbon emissions. From 2005 to 2020, the total carbon emissions in Guangxi show an upward trend, presenting a “high in the south and low in the north” distribution characteristic, gradually forming a high-level region in the capital city of Nanning, the city of Liuzhou, and some coastal cities (such as the cities of Qinzhou, Beihai, and Fangchenggang) as the core of carbon emissions; (2) NDVI, NLI, GDP, and POP have a relatively high impact on the carbon emission pattern in Guangxi, and the impact of human activity intensity on carbon emissions is higher than that of the influencing factors of NDVI; (3) The interaction between NDVI, NLI, GDP, and POP has a significant impact on the carbon emission pattern. The aforementioned results can provide decision-making suggestions for the social and economic development of Guangxi, as well as the formulation of carbon sequestration policies.

Response of Soil Respiration to Altered Snow Cover in a Typical Temperate Grassland in China

The snow cover in temperate areas is undergoing significant changes, which may affect soil respiration (Rs), the second largest carbon flux in global carbon cycling. However, currently, there are relatively few in situ field studies on the effects of altered snow cover on Rs in temperate areas during the non-growing season compared to the research on Rs during the growing season. Therefore, it limited the accurate prediction of the characteristics and magnitude of changes in soil carbon emissions in temperate areas under global change scenarios. Here, an in situ field experiment was conducted in a typical grassland in Inner Mongolia in China to explore the characteristics of Rs under three different snow cover treatments, i.e., increasing snow (IS), decreasing snow (DS), and ambient snow that was regarded as the control check treatment (CK). The results showed that the range of Rs flux and cumulative emission flux in all treatments in the non-growing season in the study area ranged from 5.87 ± 0.20 to 55.11 ± 6.42 mg CO2 m−2 h−1 and from 22.81 ± 0.68 to 26.36 ± 0.41 g C m−2, respectively. During the observation period, the depth of the largest snow cover for each treatment did not exceed 18 cm, and none of the snow treatments caused significant variations in Rs flux (p > 0.05). However, the cumulative flux of Rs in the whole non-growing season was only stimulated significantly by 15.6% by the IS treatment compared with that of CK. The relatively high Rs flux in the non-growing season was observed to mainly occur in the soil deeply frozen period (DFP) and the soil melting period (SMP). Further analysis revealed that Rs flux under different snow treatments were mainly positively correlated with soil temperature during SMP. The main factors controlling Rs varied with different sampling periods. Our findings suggest that the non-growing season is also an important period of non-negligible carbon emissions from typical grassland soils in temperate zones.

Asymmetric business cycle changes in US carbon emissions and oil market shocks

Previous research shows that, in the USA, the elasticity of carbon emissions with respect to GDP is greater when GDP declines than when GDP increases. Using monthly US data, we examine each individual recession since 1973. We find asymmetric changes in carbon emissions in the 1973–1975, 1980, 1990–1991, and 2020 recessions but not in the 1981–1982, 2001, or 2008–2009 recessions. The former four recessions are associated with negative oil market shocks. In the first three, there was a supply shock and in 2020, a demand shock. Changes in oil consumption that are not explained by changes in GDP explain these asymmetries. Furthermore, the asymmetries are due to emissions in the transport and industrial sectors, which are the main consumers of oil. We conclude that emissions behaved similarly in 2020 to the way they did in recessions associated with oil supply shocks, but, actually, this pattern is not inherent to the business cycle itself.

Can increased economic complexity and reduced carbon emissions of the logistics industry go hand in hand? Evidence from countries along the Belt and Road

At present, the main focus of research lies in examining the connection between economic complexity and carbon emissions as a whole, but there is a scarcity of quantitative investigations on the link between the above variables within specific industries. Therefore, this study introduces economic complexity as a new variable to build a panel model within the traditional Environmental Kuznets Curve framework. Based on the data of the countries along the Belt and Road from 1998 to 2018, we used the Granger causality test to examine the causal relationship between variables, and use the Fully Modified Ordinary Least Square and Dynamic Ordinary Least Square methods to estimate the coefficients of variables. The key factor linking economic complexity and carbon emissions in the logistics industry is technology innovation Economic complexity can explain and predict the changes in carbon emissions of logistics industry more reasonably, and the relationship between them in line with the environmental kuznets curve hypothesis. Only high-income countries can increase economic complexity while reducing carbon emissions of logistics industry. Based on the empirical analysis, it is suggested that upper-middle income and lower middle-income countries can formulate relevant policies and regulations, and high-income countries can improve the relevant policies and regulations to promote the reduction of carbon emissions of the logistics industry. Studying the impact of economic complexity on carbon emissions in the logistics industry can help better predict and respond to the impact of climate change on the logistics industry.

Identifying the spatiotemporal carbon footprint of the petroleum refining industry and its mitigation potential in China

As the energy-intensive industry, the rapid growth of petroleum refining caused massive carbon emission. This study is designed to unveil carbon footprint and its temporal-spatial distribution at the refinery level and the industry level in China from the life cycle perspective, and investigate the main factors of carbon emission changes through the logarithmic mean deviation index (LMDI). The results showed that the provincial/regional distribution of the carbon footprint decreased from the eastern coastal and northwest to the southwest. Simultaneously, carbon emissions from PRI increased by 216 Mt from 2000 to 2020, with an average annual growth rate of 6.93%. Among them, the electricity and thermal phase was the main source of carbon emissions (average 48.45%). In addition, carbon emission intensity was first suppressed and then increased, which the extension of the industrial chain and the inadequate integration of refining were drivers of the increase in carbon emission intensity. The LMDI analysis indicated that investment efficiency and energy intensity were key factors on carbon emission reduction. Further scenario analysis showed that government policies and technological breakthroughs in the short term can slow down the growth in carbon emissions 19.12% and 28.32% lower than the baseline scenario, respectively.

High-frequency sectoral carbon and environmental analysis based on monthly input-output tables compilation during 2018–2021

This paper proposes a process for updating monthly input-output tables with monthly macroeconomic statistics and published input-output tables. Reasonable assumptions are set up and 48monthly input-output tables are prepared from 2018 to 2021 with the combination of the row range series method and nonlinear mathematical planning. The Weaver-Thomas composite index is used to analyze the role of the sector in the economic network, and the sectoral correlation indicators are used to analyze the correlation change of the sector's monthly electricity emissions to show an environmental application effect of the monthly input-output table. The results show that the monthly input-output tables can be prepared with acceptable accuracy, and they can reveal the sectoral network structure changes and sectoral carbon emissions changes in continuous monthly time series. The proposed approach contributes for the compilation of high-time-frequency input-output tables, so as to support high-frequency industrial environmental impact analysis.

Modeling the potential impacts of climate change on wheat yield in Iran: Evidence from national and provincial data analysis

Wheat is one of the essential food resources, and its cultivation is one of Iran's most vulnerable activities to climate change. In this context, this study aims to unravel the effects of climate change on both irrigated and rain-fed types of wheat yield in Iran and across different regions to provide valuable insights to researchers, policymakers, and farmers. The study will assist in comprehending the adaption and spatial difference effects of climate change on wheat yield. For this purpose, provinces were classified into two climate types based on the De Martonne Aridity Index. The Feasible generalized least squares model was then employed to data from 28 Iranian provinces over the period 2001 to 2019. The index results show that most provinces are in the arid climate group, while the rest are in the semi-arid group. Our findings for the country indicated that changes in climate factors could have positive and negative effects on wheat yield, with results varying by region. Both wheat varieties are acclimated to climate change's effects, and the coefficients of climate factors from 2001 to 2019 are smaller than those from 2008 to 2019. Conversely, semi-arid provinces' irrigated and rain-fed wheat yields were not adapted to the impact of temperature and participation level changes, while irrigated and rain-fed wheat yields were adapted to carbon emissions. In arid provinces, temperature coefficients for rain-fed wheat yield increased from -1.68 to -2.90, while for irrigated type, coefficients decreased from -0.32 to -0.22. The yield of irrigated wheat has not adapted to changes in carbon emissions and precipitation levels. The study's findings can be useful to policymakers and farmers as they plan and alter crop farming to adapt to the effects of changing climate conditions.

Evaluation of the impact of moral dissonance-based low-carbon interventions on consumer behavior

With the ever-changing scientific and technological progress and improving human living standards, people's consumption levels are also steadily increasing. However, the consumption behavior of consumers has brought a significant impact on the earth's environment. For this reason, some governments or organizations are constantly calling for environmentally friendly consumption behaviors, trying to change people's consumption habits through a series of intervention measures. However, researchers can hardly track each consumer's behavioral changes and inner thoughts in real-time, so it is challenging to quantify the effect of intervention measures. This study considers three factors: consumer behavior, morality and self-esteem, and intervention measures to estimate the carbon emissions caused by consumption behavior. Firstly, the carbon emissions are estimated through the existing behavior of consumers. Secondly, the morality and self-esteem of the respondents are evaluated through the process of moral dissonance and the self-esteem scale. And then we investigate the change of consumers' carbon emissions under the intervention measures. Finally, the adjusted final result is given by combining morality and self-esteem. Through a series of surveys on 354 respondents, this study estimated that their average annual carbon emissions without intervention were 5.3 tons, and their average yearly carbon emissions after intervention were 4.365 tons. After considering the intervention of moral disorder and self-esteem, the average annual carbon emission is 4.691 tons. From the results, this study explains why some measures are not as effective as expected and, at the same time, provides a new research method for quantifying the effect of intervention measures.

Research on Regional Carbon Emission Reduction in the Beijing–Tianjin–Hebei Urban Agglomeration Based on System Dynamics: Key Factors and Policy Analysis

Urban agglomerations are regions where the economy and population are highly concentrated, which are also spatial units with more concentrated carbon emissions. A detailed decomposition of driving factors based on changes in carbon emissions of urban agglomerations can provide a reference for better carbon reduction policies. In this paper, we establish an evaluation framework of carbon emission drivers of urban agglomeration from the perspective of CO2 generation and removal using a system dynamics method. The key influencing factors and optimal emission reduction measures of carbon emissions in urban agglomerations are explored. The results are as follows: (1) The industrial structure is the key influencing factor of carbon emissions; (2) compared with no implementation of any policies, the total carbon emissions and carbon emission intensity of integrated policies all significantly decrease, with a decrease of 43.68% and 53.32%, respectively in 2035; (3) energy structure adjustment has a significant effect in reducing carbon emissions and carbon emission intensity; (4) the role of increasing investment in technological innovation in ensuring achievement of “carbon peak” should not be ignored. It is found that integrated policies often exhibit a better emission reduction effect, but this effect is not a simple summation of the effects of each single policy.

How Changes in Carbon Emissions Affect the Output of Mining Industry and Analysis on the Incentive Effect

Social Problems and Impacts: Nowadays, the economy is burgeoning around the globe. However, simultaneously, air pollution still remains pervasive on a global scale, exerting negative impact on human health, among which Carbon dioxide is in great proportion. As factories constant output will lead to growing carbon emissions, air pollution is becoming increasingly serious. In this study, we will focus on the relationship between the change in carbon emissions and the output in mining industry, to see that whether carbon reduction policy will affect economy, and whether incentive effect will happen. Research question: How does the change in carbon emissions affects the output of mining industry? Will the incentive effect happen? (Will carbon reduction policy boosts industrial transformation?) Research hypothesis: 1. An increase in CO2 emissions will promote the development of the mining industry. 2. Incentive effect will happen. Methods: Research and data-set analysis; OLS regression Model with Quantile-Quantile plots Key findings and results:1. Increase in CO2 emissions will promote the development of the mining industry. 2. Incentive effect might happen.

Who drives carbon emissions and what emission reduction potential in the resource curse agglomeration: a case of Xinjiang.

The primary problem in achieving carbon emission reduction and carbon peak is to identify the key driving factors and emission reduction potential of the industrial sector, especially in resource-cursed regions like Xinjiang. This study aimed to explore the key driving factors and abatement potential of carbon emissions based on the "energy-environment-economy" hybrid input-output model of Xinjiang during 1997-2017. The result showed that: (1) Industrial carbon emissions have experienced three stages: slow growth-rapid growth-stable growth. (2) Demand change effect and energy intensity effect were the determinants of Xinjiang industrial carbon emission change; Capital formation and domestic trade were the biggest drivers of carbon emissions growth; Especially after entering the "new normal",the driving force of imports in Xinjiang's international trade increased gradually over time. (3) The coal-based energy structure was both the biggest obstacle and the best entry point in carbon emission reduction. (4) Of the 28 key industry sectors, heavy industry including the production and supply of electricity and heat (S22), petroleum processing, coking and nuclear fuel processing (S11), chemical industry (S12), metal smelting and rolling (S14), and energy industries had the greatest potential for carbon reduction. The research findings provide scientific decision-making reference for Xinjiang to accurately grasp the carbon emission reduction potential of the industry and formulate a targeted carbon peak action plan.

Inferring the role of fiscal decentralization and carbon taxes through the ARDL approach

Abstract This paper first introduces the algorithmic steps of the ARDL model, explores the long-run relationship of variables using the cointegration test and estimates the long-run relationship, and then builds an error correction model to explore the short-run relationship in the model to estimate the model coefficients. Finally, we analyze the changes in carbon emissions and the relationship between carbon emissions and economic growth in China in time and space, respectively, and use the ARDL model to empirically test the correlation between fiscal decentralization and carbon tax Between 1978 and 2004, the growth rate of the economy remained above 10%, and the growth rate of carbon emissions also increased rapidly at this time, and carbon emissions were positively correlated with economic growth, and the growth rate of economic growth was positively correlated with the growth rate of carbon emissions. The growth rate of carbon emissions is positively correlated with economic growth, and the trend of the growth rate of economic growth and carbon emissions is roughly the same. The coefficient of fiscal decentralization degree on carbon tax expenditure is inhibited at a 1% significant level. The coefficient of the industrial structure plays a catalytic role in carbon tax expenditure at a 1% significant level, and for every 1% unit decrease in the proportion of secondary industry to GDP, carbon tax expenditure decreases by 2.473%. The implementation of a carbon tax can achieve the goal of energy saving and emission reduction, but we should also pay attention to the possible counter-effects of a carbon tax on economic development and reasonably levy carbon tax to make the economy and environment reach a balanced state.

Assessing Spatiotemporal Characteristics and Driving Factors of Urban Public Buildings Carbon Emissions in China: An Approach Based on LMDI Analysis

Urban public buildings carbon emissions exhibit an upward trend and have a large potential in carbon emission reduction. The analysis of spatiotemporal characteristics and driving factors for urban public buildings carbon emissions is essential in formulating effective policies for carbon reduction, meeting commitments to peak carbon emissions and achieving carbon neutrality. This study takes China’s urban public buildings carbon emissions as the research object, employing methods such as spatial autocorrelation analyses, kernel density estimation analyses, and the LMDI decomposition methods to explore the spatiotemporal characteristics and regional disparities in carbon emissions from 2006 to 2019. Furthermore, it quantifies the contributions of driving factors to the spatiotemporal changes in urban public buildings carbon emissions. The results show the following: (1) Urban public buildings carbon emissions among provinces are consistently increasing, indicating an overall upward trend. The spatial distribution highlights significant regional disparities. (2) The spatial characteristics of urban public buildings carbon emissions were basically stable. The eastern coastal regions demonstrate a high-high cluster, while the western regions exhibit a low-low cluster. The overall cluster evolution showed a decreasing trend from east to west. (3) Per capita urban public building area, economic density, urbanization rate, and population size serve as driving factors for carbon emissions from urban public buildings, while energy efficiency and energy consumption intensity act as inhibitory factors. The findings of this research can assist policymakers in getting a deeper comprehension of urban public buildings carbon emissions and providing a scientific basis to formulate appropriate carbon emission reduction policies.