Changes In Carbon Emissions Research Articles

An inter-regional input-output table series of China from 1987–2017 with integrated carbon emission data

Inter-regional input-output (IRIO) tables are essential for socioeconomic and environmental analysis. This paper compiled a continuous time series of Chinese IRIO tables with a detailed regional and sectoral classification, covering a longer period from 1987 to 2017 than existing Chinese IRIO tables. Additionally, we integrated the China provincial CO2 emission inventory data (1987–2017) to analyze trends and identify the main driving factors behind regional economic linkages and carbon emissions changes. Specifically, we adjusted the initial values of the international and interprovincial imports and exports using customs data. Subsequently, we employed the minimum cross-entropy model to balance the adjusted input-output tables (IOTs) from both row and column perspectives. Following this, we estimated the interprovincial trade flow for each sector using the gravity model. Then, we balanced these estimates with the minimum cross-entropy model to form the multi-regional input-output (MRIO) table. Finally, we constructed the IRIO table based on the proportion assumption. The compiled IRIO tables, integrated with carbon emission data, have significant applications in regional economic analysis, policy evaluation, and environmental studies.

Spatial-temporal analysis of carbon benefit and land use for low-carbon development strategies in Shandong Province, China

Abstract Assessing the carbon sequestration capacity of regional ecosystems is essential for achieving carbon neutrality goals. However, existing research often fails to comprehensively evaluate the spatial-temporal dynamic changes of ecosystem carbon emissions and absorption. This study introduces a Carbon Benefit Index (CBI) to assess carbon neutrality potential and classifies Shandong's 16 cities into four regions based on their carbon storage and emissions profiles. We conducted an in-depth analysis of ecosystem carbon benefits in Shandong Province from 2000 to 2020 using the multimodel random forest ensemble method, which enhances the accuracy of carbon sink simulations across terrestrial ecosystems. Our results showed that from 2000 to 2020, Shandong’s carbon emissions increased by 1.45×10⁸ tons (a 203.8% rise), while carbon storage decreased by 3.40×10⁷ tons (a 2.05% decline). Compared to previous studies, our findings underscore the significance of both above-ground and below-ground carbon storage. Grey correlation analysis of land use, anthropogenic CO₂ emissions, and ecosystem carbon storage revealed that cultivated and forest lands were most significantly correlated with carbon storage, whereas built-up areas were most closely linked to carbon emissions. The CBI analysis and classification of the 16 cities into four categories highlights the spatial-temporal heterogeneous of the carbon efficiency, and diverse roles cities play in the province's overall carbon balance, informing city-specific, targeted carbon reduction strategies. The study emphasizes the need for spatially differentiated, comprehensive carbon accounting to improve carbon efficiency. Based on these findings, we propose tailored low-carbon improvement strategies for different regions. This research not only contributes to existing literature by incorporating below-ground carbon storage but also provides valuable insights for policy and land management, with practical implications for promoting sustainable development and advancing efforts toward carbon neutrality.

Development of a regional carbon assimilation system and its application for estimating fossil fuel carbon emissions in the Yangtze River Delta, China

Urban areas are the largest contributors to global fossil fuel carbon emissions, and controlling urban carbon emissions is critical to addressing climate change and reducing greenhouse gas emissions. However, estimates of urban emissions remain large uncertainties, making it difficult to accurately understand changes in urban carbon emissions and to assess the effectiveness of emission controls. Atmospheric CO2 observations, through data assimilation, can objectively invert changes in urban carbon emissions. In this study, inheriting the inversion method of a global carbon assimilation system of GCASv2, we constructed a regional carbon assimilation system based on a regional 3-D atmospheric chemical transport model WRF-CMAQ, named RegGCAS. We tested the performance of the established assimilation system for winter carbon emissions in the Yangtze River Delta (YRD) by performing a 9 km × 9 km resolution inversion in December 2020, using observations from 6 ground stations and two emission inventories as a priori, respectively. The results show that after assimilating the observations in the YRD region, especially in Suzhou and Hangzhou where there are more observing sites, the two significantly different a priori emissions are effectively converged, with the relative difference being reduced from −45 % and − 36 % of the a priori emissions to −10 % and − 15 % of the a posteriori emissions. Furthermore, the daily changes of the inverted emissions clearly show that weekend emissions are lower than weekday emissions, i.e. the inversion can reveal the weekend effect of emissions changes. This suggests that our system is well established and can be used for estimating fossil fuel carbon emissions during winter. Meanwhile, these results also imply that with our system, when a city has three or more stations, its total emissions can be constrained reliably.

Factors and structural paths of the changes in carbon emissions in China's provincial construction industries

The changes in the carbon emissions in China's provincial construction industries are of high complexity. It is essential to understand the changes in the construction carbon emissions (CCEs) in China on the provincial scale. This study evaluates the factors and structural paths of the changes in provincial CCEs in China between 2012 and 2017 using the structural path decomposition analysis. The results show that the emission intensity effect and production structure effect contributed greatly to the reduction of CCEs across various regions, while the final demand effect had contrary impacts. The local nonmetallic mineral products industry (c13), metal smelting and pressing industry (c14), and electricity industry (c24) generally contributed significantly to the emission intensity effect, production structure effect, and final demand effect across most regions. The consumption of local c13, c14, and c24 by the construction industry (c27), namely “local c13→c27”, “local c14→c27”, and “local c24→c27” were generally the important structural paths of the CCEs changes across various regions. Nonlocal industries such as Hebei c14 and nonlocal structural paths such as “Hebei c14→c27” contributed substantially to the CCEs changes in many regions such as Beijing. The emission intensity effect, first-order production structure effect, and final demand effect typically dominated the effects of the critical structural paths of the CCEs changes across various regions. This study can help policymakers better understand the changes in China's provincial CCEs to formulate region-specific emission reduction measures and provide a comprehensive reference for related research.

Carbon Emission for China's Iron and Steel Industry： Peak Scenarios and Neutralization Pathways

To explore the future carbon emission peak scenarios of China's iron and steel industry as well as the effective pathways for carbon emission neutrality, the generalized Divisia index method （GDIM） was first used to analyze the influencing factors of carbon emission changes from 2001 to 2020, and then Monte Carlo simulation was used to conduct a dynamic scenario simulation of the carbon emission evolution trends from 2021 to 2035. The results showed that： ① Economic output and crude steel production were the most important factors contributing to the increase in carbon emission in the iron and steel industry； among the factors contributing to the decrease, the carbon intensity of economic output had the most significant effect, followed by the carbon intensity of production, and the energy consumption per ton of steel and the energy output rate did not have a significant effect on the decrease in carbon emissions. ② Under the scenario BAU, scenario L, and scenario S, the iron and steel industry could achieve carbon emission peaking in 2030, 2025, and 2020, respectively.

Spatial and Temporal Evolution Characteristics of Agricultural Carbon Offset Rate and Prediction of Carbon Offset Potential in the Yangtze River Economic Belt

Based on the panel data of prefecture-level cities and above and provincial-level cities in the Yangtze River Economic Belt, respectively, this study measured the agricultural carbon emissions, carbon sinks, and carbon offset rates from 2006 to 2021 and analyzed their evolution characteristics. Based on the STIRPAT model and ridge regression analysis, this study identified the factors affecting agricultural carbon emissions in the 11 provinces and municipalities in the Yangtze River Economic Zone, combined them with the scenario analysis method to predict the agricultural carbon emissions under the baseline scenario in the period of 2022 to 2025, and analyzed the process of "carbon peaking." Simultaneously, this study predicted the agricultural carbon sinks of 11 provinces and cities from 2022 to 2025 and then speculated their agricultural "carbon neutral" process under the framework of agricultural carbon compensation rate, so as to summarize the effective paths for different provinces and cities to achieve agricultural "carbon peak and carbon neutral." The results showed that： ① Changes in agricultural carbon emissions in the Yangtze River Economic Zone during the observation period followed an inverted U-shape and peaked in 2015 at 33 312.65×104 tons. The fluctuation of agricultural carbon sinks was relatively small, with an overall upward trend. The upward trend of the agricultural carbon offset rate was obvious, but it still belonged to the "net carbon emission" region. ② Regional differences of agricultural carbon offsetting rate were prominent, and there was a polarization phenomenon, with "net carbon sink" cities significantly less than "net carbon emission" cities. ③ Shanghai, Zhejiang, and Sichuan reached the peak of agricultural carbon emissions in 2006, which Anhui and Chongqing reached in 2012, and the rest of the provinces and municipalities showed a clear upward trend. ④ Anhui, Chongqing, Sichuan, and Yunnan crossed the "agricultural carbon neutral line" and achieved agricultural carbon neutrality. Jiangsu was expected to achieve this in 2026-2030, whereas the remaining provinces and municipalities faced greater difficulties.

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.