Changes In Carbon Emissions Research Articles

Pathways to innovation: How city pilots leverage digital technology to reduce carbon emissions

Technological innovation through various pathways is crucial for achieving carbon neutrality and peak carbon emissions. The Innovative City Pilot (ICP) initiative, a key effort to promote national innovation, has garnered increasing attention. However, most studies have not examined the effects of ICP on carbon emissions through diverse digital technologies. Using city-level data from 2000 to 2022, we constructed a staggered difference-in-differences (DID) model to analyze changes in carbon emissions before and after the ICP period in both ICP and non-ICP areas. The carbon emission reduction pathway of the ICP influences various forms of digital technology, which then empower local green product innovation, market-oriented innovation, and industrial innovation. The impact of ICP emission reduction through digital technology is more significant in western, first- and second-tier, sub-provincial, populous, and coastal cities. In conclusion, policymakers should tailor policies according to the action pathways of digital technology and specific city characteristics. We outline the specific pathways of green product innovation, market-oriented innovation, and industrial innovation enabled by digital technologies and provide policy recommendations for the sustainable development of China.

Dynamic Simulation of Carbon Emission Peak in City-Scale Building Sector: A Life-Cycle Approach Based on LEAP-SD Model

Systematically predicting carbon emissions in the building sector is crucial for formulating effective policies and plans. However, the timing and potential peak emissions from urban buildings remain unclear. This research integrates socio-economic, urban planning, building technology, and energy consumption factors to develop a LEAP-SD model using Shenzhen as a case study. The model considers the interrelationship between socio-economic development and energy consumption, providing more realistic scenario simulations to predict changes in carbon emissions within the urban building sector. The study investigates potential emission peaks and peak times of buildings under different population and building area development scenarios. The results indicate that achieving carbon peaking by 2030 is challenging under a business as usual (BAU) scenario. However, a 10% greater reduction in energy intensity compared to BAU could result in peaking around 2030. The simulation analysis highlights the significant impact of factors such as population growth rate, per capita residential building area, and energy consumption per unit building area and the need for a comprehensive analysis. It provides more realistic scenario simulations that not only enhance theories and models for predicting carbon emissions but also offer valuable insights for policymakers in establishing effective reduction targets and strategies.

Spatio-temporal variations of energy carbon emissions in Xinjiang based on DMSP-OLS and NPP-VIIRS nighttime light remote sensing data.

With the rapid economic development of Xinjiang Uygur Autonomous Region (Xinjiang), energy consumption became the primary source of carbon emissions. The growth trend in energy consumption and coal-dominated energy structure are unlikely to change significantly in the short term, meaning that carbon emissions are expected to continue rising. To clarify the changes in energy-related carbon emissions in Xinjiang over the past 15 years, this paper integrates DMSP/OLS and NPP/VIIRS data to generate long-term nighttime light remote sensing data from 2005 to 2020. The data is used to analyze the distribution characteristics of carbon emissions, spatial autocorrelation, frequency of changes, and the standard deviation ellipse. The results show that: (1) From 2005 to 2020, the total carbon emissions in Xinjiang continued to grow, with noticeable urban additions although the growth rate fluctuated. In spatial distribution, non-carbon emission areas were mainly located in the northwest; low-carbon emission areas mostly small and medium-sized towns; and high-carbon emission areas were concentrated around the provincial capital and urban agglomerations. (2) There were significant regional differences in carbon emissions, with clear spatial clustering of energy consumption. The clustering stabilized, showing distinct "high-high" and "low-low" patterns. (3) Carbon emissions in central urban areas remained stable, while higher frequencies of change were seen in the peripheral areas of provincial capitals and key cities. The center of carbon emissions shifted towards southeast but later showed a trend of moving northwest. (4) Temporal and spatial variations in carbon emissions were closely linked to energy consumption intensity, population size, and economic growth. These findings provided a basis for formulating differentiated carbon emission targets and strategies, optimizing energy structures, and promoting industrial transformation to achieve low-carbon economic development in Xinjiang.

Exploring resource-efficient urban mobility for sustainable low emission solutions: A case study of Ile-de-France, France

Urban areas and their associated mobility stocks exert substantial environmental impacts, necessitating urgent mitigation efforts. However, prevailing research predominantly concentrates on operational requirements for urban mobility. Few stock studies have quantified the embodied environmental requirements of mobility stocks. This study quantified both the embodied and operational environmental requirements and examined the stock-flow-service indicators to offer a new perspective on options for low emissions on urban mobility. Focusing on the mobility dynamics of the Ile-de-France region and its three sub-regional zones from 2010 to 2018, our findings reveal a pronounced imbalance in the utilization of materials within different mobility modes (car, public transport, motorized two-wheelers, cycling, and pedestrian) across Paris, Petite Couronne, and Grande Couronne.Local disparities in the stock of mobility modes and their capacity to facilitate movement are evident, particularly when considering various service metrics (person-trip, person-travel time, and person-kilometer). Furthermore, the different patterns of service-flow-stock indicators among mobility modes are investigated to find their impacts on regional carbon emissions changes from 2010 to 2018. Stock turnover rate and service demand were the main drivers of embodied emissions of public transportation and cycling, while operational carbon intensity showed a major impact on car operational emissions. This study enriches our comprehension of the intricate interplay between resource utilization and the provision of people's mobility, thereby promoting sustainable mobility in the Ile-de-France region.

Characteristics of Spatiotemporal Changes in China's Carbon Budget at Different Administrative Scales

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.

Changes in traffic carbon emissions with traffic conditions and their control strategies

Road traffic carbon dioxide emissions have a significant impact on the atmospheric environment, and changes in traffic conditions can affect these emissions; while changes in traffic state and traffic infarction can reflect traffic conditions, there are also differences in carbon dioxide emissions generated by vehicles operating on different fuels. Therefore, this paper sequentially determines quantitative indicators of traffic states and traffic infarction as well as macro control strategies to improve traffic carbon dioxide emissions. Furthermore, taking the confluence area as the object of analysis, the impact of traffic state and traffic infarction on carbon dioxide emissions is sequentially analysed with actual traffic flow data and statistical methods, and corresponding models are constructed. Meanwhile, based on the analysis results, mainline speed limit control strategies that consider the traffic state and traffic infarction, as well as the control strategy based on the combination of multiple fuel–vehicle combinations, are presented. The analysis results show that changes in both traffic states and traffic infarction can affect traffic carbon dioxide emissions from the mainline and the on-ramps, which can fundamentally affect traffic conditions. Moreover, total carbon dioxide emissions from a combination of gasoline and diesel minibuses decrease with an increase in the ratio of gasoline minibuses.

Beyond short-term impact of COVID-19 on transport decarbonization: a scenario analysis of passenger and freight transport by mode in China, 2020–2030

BackgroundThe processes of transport decarbonisation are complicated. In this paper, we adopt the Activity-Modal Share-Energy Intensity-Carbon Intensity of Fuel (ASIF) approach and propose a conceptual framework on the direct and indirect impact of COVID-19 on transport CO2 emissions. In the Chinese context, changes of carbon emissions associated with passenger and freight transport (including urban, rural, and inter-city transport) across different transport modes are estimated. Scenario analysis is then used to estimate the impact of COVID-19 on total transport carbon emissions up to 2030. Four scenarios, from minimal to significant behavioural changes and global recession associated with COVID-19, are generated.ResultsUnder the pandemic, the transport system in China was estimated to have produced 28% less CO2 emissions (1044.2 Mt) in 2020, when compared to 2019. Compared with the business-as-usual scenario, the estimated total transport carbon emissions in 2030 would drop by 6%, 15%, and 21% and 23% under the minimal-impact, low-impact, moderate-impact, and severe-impact scenarios, respectively.ConclusionsThe results suggest that the processes triggered by COVID-19 alone will not be sufficient to meet the ambitious transport decarbonisation targets. To meet China’s pledge under the United Nations Framework on Climate Change, the medium-term effects of COVID-19 must be combined with strong transport decarbonisation measures of modal shift and new energy applications. With these additional measures, it may be possible to advance the transport carbon peak before 2030. Lessons are relevant to other developing countries.

Analysis of Spatiotemporal Changes in Energy Consumption Carbon Emissions at District and County Levels Based on Nighttime Light Data—A Case Study of Jiangsu Province in China

Approximately 86% of the total carbon emissions are generated by energy consumption, and the study of the variation of energy consumption carbon emissions (ECCE) is of vital significance to regional sustainable development and energy conservation. Currently, carbon emissions accounting mainly focuses on large and medium-scale statistics, but at smaller scales (district and county level), it still remains unclear. Due to the high correlation between nighttime light (NTL) data and ECCE, this study combines “energy inventory statistics” with NTL data to estimate ECCE at smaller scales. First, we obtained city-level statistics on ECCE and corrected the NTL data by applying the VANUI index to the original NTL data from NPP-VIIRS. Second, an analysis was conducted on the correlation between the two variables, and a model was created to fit the relationship between them. Under the assumption that ECCE will be consistent within a given region, we utilized the model to estimate ECCE in districts and counties, eventually obtaining correct results at the county-level. We estimated the ECCE in each district and county of Jiangsu Province from 2013 to 2022 using the above-proposed approach, and we examined the variations in these emissions both spatially and temporally across the districts and counties. The results revealed a significant degree of correlation between the two variables, with the R2 of the fitting models exceeding 0.8. Furthermore, ECCE in Jiangsu Province fluctuated upward during this period, with clear regional clustering characteristics. The study’s conclusions provide information about how carbon emissions from small-scale energy use are estimated. They also serve as a foundation for the creation of regional energy conservation and emission reduction policies, as well as a small-scale assessment of the present state.

Research on the Influencing Factors of Carbon Emissions in Low-carbon Communities based on Complex Network Theory

Achieving carbon peak before 2030 and carbon neutrality before 2060 is a solemn commitment made by China to address global climate change, and is also one of the main goals for economic and social development in the 14th Five Year Plan and the 2035 vision period. The changes in carbon emissions are directly related to the progress of China's "carbon peak" and "carbon neutrality" goals. Therefore, in-depth research on the influencing factors of carbon emissions has become a key link in promoting the achievement of this goal. In the existing research on carbon emission influencing factors, countries mainly focus on macro scale low-carbon urban carbon emission influencing factors and micro scale low-carbon building full life cycle carbon emission influencing factors. However, there is relatively little research on the influencing factors of carbon emissions in low-carbon communities of urban micro units, and there is still considerable research space. This study conducted an in-depth analysis of the influencing factors of carbon emissions in low-carbon communities using complex network methods. By constructing a complex network model of factors affecting carbon emissions, we identified key nodes and pathways, and explored their interrelationships. The results indicate that factors such as energy structure, resident behavior, building design, and policy implementation play an important role in carbon emissions in low-carbon communities.

The Impacts on Regional Development and “Resource Curse” by Energy Substitution Policy: Verification from China

With the increasing “resource curse” phenomena relating to energy resources in China, research concerning energy substitution policies has become more meaningful. In this paper, a dynamic CGE model was built to evaluate the impacts of energy substitution policies on regional growth and the “resource curse”. The results show the following: (1) Compared to other regions, an energy substitution policy exerts a more considerable influence on economic growth in regions with high “resource curse”. (2) The changes in carbon emissions in regions with no or low “resource curse” are modest. The primary factor contributing to the rapid decline might be energy structure adjustments. As the intensity of substitution varies, the regional disparities in marginal emission reduction costs are expected to increase. (3) Energy substitution policies reduce the severity of high “resource curse” regions. As the intensity of energy substitution increases, the extent of “resource curse” regions decreases accordingly. Some suggestions are given as follows: (1) Further promote energy substitution and speed up the transformation of energy production and consumption modes in China. (2) Accelerate the energy transformation in “resource curse” regions to promote regional sustainable development. (3) Improve energy substitution policies in transportation, industry and other fields.

Determinants of spatiotemporal changes of land use carbon emissions for counties in Shaanxi Province, China.

In China, urban sprawl and developed land expansion challenge the country's "carbon peak" and "carbon neutrality" goals. Counties as the basic governance units arecrucial for effective carbon reduction policies. This study examines land use carbon emissions (LUCE) in Shaanxi Province at the county level, essential for China's low-carbon strategy. Analyzing data from 107 counties between 2000 and 2020, we found that developed land, though increasing, is the primary carbon source with a slowing growth rate. The Conversion of Cropland to Forests and Grasslands national policy mitigated the impact on carbon absorption. Carbon emissions displayed positive autocorrelation and spatial heterogeneity, varying across the region. Using the Spatial Durbin Error Model, we linked county-level emissions to GDP per capita, population, urbanization rate, and research and development expenditure for direct and indirect influence. These factors correlate with fossil fuel use and high-quality industrial development. Promoting public transits and reducing private car use are vital for achieving local and regional low-carbon goals.

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.

Carbon Emissions Dynamics and Environmental Sustainability in China’s Tourism Sector: A 22-Year Comprehensive Regional Study

China’s tourism industry has rapidly grown over the past two decades, significantly contributing to the national economy but also increasing carbon emissions. This has raised concerns about environmental sustainability, especially given China’s commitments to peak carbon emissions by 2030 and achieve carbon neutrality by 2060. This study analyzes the changes in carbon emissions, carbon carrying capacity, and the Tourism Carbon Pressure Index (TCPI) of China’s tourism industry from 2000 to 2022, aiming to support the green transition of the sector. Examining data from 31 provinces, we observed a significant growth trend in carbon emissions, with a temporary decline during the COVID-19 pandemic in 2020, followed by a rebound in 2021 and 2022. The findings reveal that carbon emissions and carrying capacity are relatively stable in the eastern regions, but more pronounced in central and western regions, particularly the west. The TCPI is higher in the east, indicating greater environmental pressure, while it is rapidly increasing in central and western regions. To achieve sustainable tourism development, we propose promoting green transportation, eco-friendly hotels, and green scenic spots, improving resource utilization efficiency, enhancing policy support and technological innovation, and adopting regional development strategies. These measures will help reduce tourism-related carbon emissions and contribute to national carbon neutrality goals. This study provides a comprehensive analysis and actionable insights to guide the sustainable transformation of China’s tourism industry.

(Invited) Electron Microscopy Investigation of PEM Electrolyzer Degradation at Low Iridium Loadings

The employment of hydrogen as an energy carrier provides a promising solution toward zero carbon emission and battling climate change. To meet the DOE’s Hydrogen Shot goal of $1 per kilogram of hydrogen by 2031, proton exchange membrane (PEM) water electrolysis is a key technology of hydrogen production from renewable energy sources. Successful deployment of PEM electrolyzers requires reducing cost while simultaneously improving performance and lifetime. While cost reduction is expected from a variety of strategies including scaling up, operation optimization, and advanced manufacturing, it remains vital to reduce the use of noble-metal catalyst due to its scarcity and vulnerable supply chain subject to market disturbance. [1]Reducing the loading of iridium catalyst on the anode while maintaining electrolyzer performance and durability is of great challenge. In this talk, we discuss issues in PEM electrolyzer component development and advanced manufacturing of electrode layers related to catalyst loading reduction with an emphasize on analytical electron microscopy which provides valuable insight on materials morphology, crystalline structure, and chemical compositions. The batch-to-batch variance of catalyst morphology and elemental composition/distribution from commercial suppliers will be examined for catalyst down-selection. We then analyze the effect of reducing Ir loading on the electrode layer structure and ionomer distribution. These results are correlated with electrochemical measurements in the electrolyzer cell, which help elucidate the effect of low catalyst loading on electrolyzer performance. Morphologies of degraded low-loading anode catalyst layer and gas recombination layers will be investigated, and the dissolution and migration of iridium will be studied. We will also examine impact of different roll-to-roll manufacturing strategies for low-loading catalyst layers and on electrolyzer durability. Last but not least, we analyze MEAs subject to cation contamination and recovery procedure with electron microscopy. [2]References[1] Alia, S.M., Current Opinion in Chemical Engineering 2021, 33:100703.[2] Funding for this work was provided by U.S. Department of Energy Office of Energy Efficiency and Renewable Energy, Hydrogen and Fuel Cell Technologies Office through the H2NEW Consortium. Electron microscopy research conducted as part of a user project at the Center for Nanophase Materials Sciences (CNMS), which is a US Department of Energy, Office of Science User Facility at Oak Ridge National Laboratory.

Correlation between carbon emissions, fuel consumption of vehicles and speed limit on expressway

This paper aimed to investigate the correlation between carbon emissions, fuel consumption, and speed limit. A theoretical model was derived based on the energy conservation law, which expresses the relationship between vehicle's fuel consumption and speed. Subsequently, a total of 40 sets of fuel consumption data were collected through field tests to verify the accuracy of the theoretical model at different speeds and different road longitudinal slope combinations. The fuel consumption was then converted to carbon emissions according to the carbon emission factors specified by Intergovernmental Panel on Climate Change (IPCC). In the field experiment, two types of cars and trucks, which are most common on the expressways in China, were selected. Finally, the travel speed under different posted speed limits was obtained through the previously established model, and the carbon emission changes of different vehicle types at different limited speeds are calculated. The results show that the speed limit has a significant impact on fuel consumption and carbon emissions. When the speed limit increased from 80 to 120 km/h, average vehicle speeds increased about 21%–27%, and fuel consumption and carbon emissions increased from approximately 33%–38%. Another interesting result was that the vehicle's fuel consumption and carbon emissions are only affected by speed. The results of the study explore the effect of speed limits on carbon emissions and provide evidence for road managers to set reasonable speed limits.

Impact of newly extended metro line opening on commuting carbon emissions: A comparative study before and during the pandemic

Urban transportation is a key contributor to greenhouse gas emissions. Given that metro systems serve as prominent modes of public transportation, it becomes imperative to investigate the alteration in carbon emissions subsequent to the inauguration of new metro lines, along with the determinants influencing the metro commuting choices. Previous research tends to use surveys to collect individual travel data, which may have sample selection and self-reported data problems. This research combines big data and survey method to study commuting carbon emission change and influencing factors of metro adoption. Because COVID-19 also has an impact on individual commuting mode, this research further explores total and per capita carbon emissions before and during the pandemic period. Selecting Shenzhen metro lines as a study case, it is found that the opening of newly extended metro lines leads to an increased proportion of residents commuting by metro and a decreased proportion of car users, resulting in a reduction of commuting carbon emissions. Additionally, during the pandemic, people tended to choose private cars for commuting due to its advantage of independence and privacy, resulting in an increase in commuting carbon emissions. Finally, regarding influencing factors of metro adoption, it is also found that young and middle-aged, highly educated, low-income residents or residents without cars are more likely to choose metro as their commuting mode. This research suggests the local government promote public transit after the pandemic period and tailor transport policies for the various resident groups, including providing subsidies.

Analysis of the carbon emissions trend in the Indian manufacturing sector: a decomposition and decoupling approach.

There is a growing emphasis on fostering green growth and lowering carbon emissions in order to achieve sustainable economic development. This study uses the Tapio decoupling model and analyzes the factors influencing changes in carbon emissions from manufacturing in India utilizing the log mean Divisia index (LMDI) techniques. Furthermore, the nexus between carbon emission intensity, information and communication technology (ICT), total factor productivity (TFP), skill, and energy intensity has been analyzed using the system-GMM approach. It is based on the plant-level Annual Survey of Industries (ASI) datasets for the organized manufacturing sector of India from 2001 to 2002 to 2019 2020 for the major 21 Indian states/UT. The findings reflect the presence of weak decoupling in the manufacturing sector both at the aggregate level and in states. This indicates that both output and emissions are increasing; however, output growth surpasses emission growth, which signifies an effort to transition towards more environmentally friendly production methods and enhanced energy efficiency. The output and population effect are found to be leading factors in carbon emissions, while energy intensity is found to be reducing the effect. Further, the system-GMM estimates show that ICT and energy intensity positively affect total factor productivity, while with an increase in carbon emission intensity, productivity declines. The study confirms the existence of an inverted N-shaped Kuznets curve in the sector. This present study will contribute to formulating energy and environmental strategies to reduce emissions and promote adopting cleaner energy sources. These efforts will facilitate the attainment of carbon neutrality and enhance energy efficiency within the sector.

Contributing to Carbon Neutrality Targets: A Scenario Simulation and Pattern Optimization of Land Use in Shandong Province Based on the PLUS Model

Land use profoundly impacts the sustainable development of the ecological environment. Optimizing land use patterns is a vital approach to mitigate climate change and achieve carbon neutrality. Using Shandong Province as a case study, this research evaluates the impacts of land use and land cover change (LUCC) on regional carbon storage and emissions. Employing a coupled PLUS–InVEST–GM(1,1) model, simulations were conducted for scenarios including the natural scenario (NS), cropland protection scenario (CPS), high-speed development scenario (HDS), and low-carbon scenario (LCS), to assess LUCC and changes in carbon storage and emissions from 2030 to 2060 under these scenarios. The findings indicate that due to the expansion of construction land and significant declines in arable and grassland areas, carbon emissions increased by 40,436.44 × 104 t over a 20-year period, while carbon storage decreased by 4881.13 × 104 t. Notably, forests contributed the most to carbon sequestration, while construction land emerged as the primary source of carbon emissions. Simulating four scenarios demonstrates that measures such as protecting cropland, expanding forest, grassland, and aquatic areas, controlling construction land expansion, and promoting intensive development positively affect emission reductions and carbon sequestration in Shandong. These findings underscore the importance of rational planning of land use patterns, which can enhance contributions to carbon neutrality by harmonizing the relationships among cropland protection, ecological conservation, and economic development.

Exploring the Spatial and Temporal Trajectories of Land Use Carbon Emissions and Influencing Factors in the Aksu River Basin from 1990 to 2020

Land use changes lead to changes in the functions of different types of carbon sources and sinks, which are key sources of carbon emissions. The study of carbon emissions and its influencing factors in the Aksu River Basin from the perspective of land use change is of great importance for the promotion of integrated protection and restoration of mountains, water, forests, fields, lakes, grasslands, sand, and ice in the basin and to help achieve the goal of carbon peaking and carbon neutrality. Based on four periods of land use data and socio-economic data from 1990 to 2020, the total carbon emissions from land use were measured, and the spatial and temporal trajectories of carbon emissions and their influencing factors were explored. The results showed that:① from 1990 to 2020, arable land, forest land, construction land, and unused land showed a general increasing trend, whereas grasslands and water areas showed a decreasing trend. The spatial change in land use types was mainly characterized by the conversion of grasslands and unused land into arable land, and 83.58 % of the arable land conversion areas were concentrated in the southwest of Wensu, Aksu, and the northern part of Awat. ② The total net carbon emissions in the basin showed a continuous growth trend from 1990 to 2020, with a cumulative increase of 14.78×104 t. The increase in arable land was a key factor causing an increase in net carbon emissions in the basin. ③ The spatial distribution pattern of land use carbon emissions in the basin was high in the middle and low in the fourth, with significant changes in net carbon emissions mainly in the southern part of Wensu, Aksu, Awat, and Alaer. ④ Human activities had the strongest driving effect on land use carbon emissions, with their effects gradually increasing from east to west. The contribution of average annual temperature to land use carbon emissions was mainly concentrated in the eastern part of Aksu and the northern part of Awat, whereas average annual rainfall had a strong inhibitory effect on the northern part of Wensu and the western part of Aheqi.

Analysis of Influencing Factors and Prediction of the Peak Value of Industrial Carbon Emission in the Sichuan-Chongqing Region

The greenhouse effect has a negative impact on social and economic development. Analyzing the factors influencing industrial carbon emissions and accurately predicting the peak of industrial carbon emissions to achieve carbon peak and carbon neutrality is therefore vital. The annual data from 2000 to 2022 were used to study the influencing factors of carbon emission and the path of carbon emission reduction. In this study, the gray correlation method and stepwise regression method were used to explore the effective factors that met the significance test and the STIRPAT expansion model was constructed to analyze the characteristics and influencing factors of industrial carbon emissions in the Sichuan-Chongqing region. Finally, the changing trend of regional industrial carbon emissions is predicted by scenario analysis and four development scenarios are set up, which show that (1) from 2000 to 2022, the change in total industrial carbon emissions in Sichuan Province and Chongqing Municipality presents an inverted U-shaped trend, reaching a phased peak in 2013 and 2014, respectively, then declining and then rising again after 2018. (2) Industrial scale foreign trade dependence and population are the effective factors of industrial carbon emission in Sichuan, and all have positive effects. Energy structure and per capita income are the effective factors in Chongqing, showing negative and positive effects, respectively. (3) Analysis of four scenarios shows that the time range of the industrial carbon peak in the Sichuan-Chongqing region is 2030–2035 and that its peak height ranges from 81.98 million tons to 87.64 million tons. Among them, the green development scenario is the most consistent path to achieve the carbon peak as soon as possible; in this case, industrial carbon emissions will peak in 2030, in line with the national target time, and the lowest peak level of 81.98 million tons. The suggestions in this paper are continuously optimizing the energy structure, adjusting the industrial scale, and accelerating scientific and technological progress to achieve sustainable development.