Spatial Distribution Of Emission Research Articles

Spatiotemporal dynamic evolution and influencing factors of land use carbon emissions: evidence from Jiangsu Province, China

Land use/cover change has an important impact on global climate change and carbon cycle, and it has become another major source of carbon emission after energy consumption. Therefore, this study focuses on the main line of “land use carbon emissions-spatial and temporal patterns-influencing factors,” and selects 13 cities in Jiangsu Province as the research object. Based on the data of land use and energy consumption, combined with the method of land use carbon emissions and ArcGIS technology, this study conducted a quantitative analysis of the spatio-temporal distribution of carbon emissions in Jiangsu Province. The factors affecting the spatial distribution of carbon emissions from land use in Jiangsu Province were discussed by using Geographic detector. The results show that: 1) Carbon emissions in Jiangsu Province showed an overall growth trend, from 16215.44 ×104tC in 2010–23597.68 ×104tC in 2020, with an average annual growth rate of 4.55%, of which the construction land and watersheds had a greater impact on carbon sources and sinks, respectively. 2) During the period, there were significant differences in carbon emission levels among different cities in Jiangsu Province, and the land use carbon emission in Jiangsu Province showed a stable spatial pattern of “northwest—southeast.” The southern part of Jiangsu is always the hot area of carbon emission, while the cold spot area is mainly distributed in the northern and central parts of Jiangsu. 3) The interaction of factors such as economic development, industrial structure, energy intensity, land use and human activities is an important reason for the spatio-temporal differences of land use carbon emissions in Jiangsu Province. Among them, the level of urbanization, population size and economic aggregate have significant effects on carbon emissions.

Underestimation of Thermogenic Methane Emissions in New York City.

Recent studies have shown that methane emissions are underestimated by inventories in many US urban areas. This has important implications for climate change mitigation policy at the city, state, and national levels. Uncertainty in both the spatial distribution and sectoral allocation of urban emissions can limit the ability of policy makers to develop appropriately focused emission reduction strategies. Top-down emission estimates based on atmospheric greenhouse gas measurements can help to improve inventories and inform policy decisions. This study presents a new high-resolution (0.02 × 0.02°) methane emission inventory for New York City and its surrounding area, constructed using the latest activity data, emission factors, and spatial proxies. The new high-resolution inventory estimates of methane emissions for the New York-Newark urban area are 1.3 times larger than those for the gridded Environmental Protection Agency inventory. We used aircraft mole fraction measurements from nine research flights to optimize the high-resolution inventory emissions within a Bayesian inversion. These sectorally optimized emissions show that the high-resolution inventory still significantly underestimates methane emissions within the New York-Newark urban area, primarily because it underestimates emissions from thermogenic sources (by a factor of 2.3). This suggests that there remains a gap in our process-based understanding of urban methane emissions.

Spatial distribution of CH4 emissions from livestock farming in Poland: a comparison of 2010 and 2020

ABSTRACT Among greenhouse gases (GHGs), methane (CH4) is a major contributor to global warming. Agriculture accounts for about 40% of CH4 emissions, with livestock being a crucial source. CH4 emissions from livestock primarily arise during enteric fermentation and manure management in livestock. This study aims to estimate Poland’s CH4 emissions from enteric fermentation and manure management in 2010 and 2020 at the municipal level (LAU-2). Data from the National Agricultural Census (NAC) and the UNFCCC National Inventory Report (NIR) for 2010 and 2020 were used for the analysis. The results reveal a slight overall increase in total CH4 emissions from livestock farming in Poland over the decade. The highest emissions were observed in regions where livestock farming was predominant, while significant changes in emissions occurred in regions experiencing considerable livestock production growth. Conversely, the lowest emissions were found in areas where the decrease in livestock population was most prominent. Understanding the spatial distribution of CH4 emissions from livestock farming at the municipal level is essential for effective climate change mitigation efforts. This study highlights the importance of continuous monitoring and analysis of livestock farming in Poland to assess its impact on GHG emissions and facilitate informed policy-making for sustainable agricultural practices.

Integrated evaluation of city-level CO2 emissions and sinks from 2010 to 2019: Spatio-temporal patterns in the Yangtze River Economic Belt of China

The intensifying impact of global climate change has led to increased attention being paid to carbon dioxide (CO2) emissions and sinks. However, it remains a challenge to accurately assess and map the spatial distribution of CO2 emissions and sinks. Previous studies have primarily focused on national and provincial level research, leaving city-level studies relatively overlooked. Acknowledging the significance of interregional differences and the crucial ecological and economic aspects of the Yangtze River Economic Belt (YREB), we devised a research framework to analyse spatio-temporal patterns through city-level CO2 emissions and sinks. The city-level CO2 emissions were estimated using provincial data and linear allocation models, while CO2 sinks were estimated based on the areas of ecosystems and their corresponding sink coefficients. The analysis of YERB's spatio-temporal patterns focuses on regional hotspots, outliers, and time-series clustering. The findings show that: (1) Between 2010 and 2019, ecosystems within YREB absorbed approximately 41.17%–46.70% of CO2 emissions, with the increase rate of CO2 emissions being lower than that of sinks. (2) City-level CO2 in Western Region of YREB exhibited low emissions and high sinks, while Eastern Region showed high emissions and low sinks, and these characteristics were intensifying. (3) City-level CO2 emissions and sinks demonstrated a “core-periphery” clustering pattern. (4) The regional CO2 balance was predominantly driven by most cities in Eastern Region. This study provides a comprehensive understanding of CO2 emissions and sinks at the city level and offers valuable insights for developing regional collaborative emission reduction strategies in YREB.

Emission characteristic, spatial distribution, and health risk of polycyclic aromatic compounds (PAHs, NPAHs, and OPAHs) from light-duty gasoline and diesel vehicles based on on-road measurements

Vehicle exhaust is the primary source of polycyclic aromatic compounds (PACs). Real road tests using a portable vehicle measurement system on light-duty gasoline vehicles and light-duty diesel trucks were conducted to investigate gas- and particle-phase polycyclic aromatic hydrocarbons (PAHs), nitro-PAHs (NPAHs), and oxy-PAHs (OPAHs) in vehicle exhaust with different emission standards, fuel types, and altitudes. The results showed that with the tightening of emission standards, the overall emission factors (EFs) of PACs decreased. Compared with China V diesel vehicles, the emissions of PAHs, OPAHs, and NPAHs from China VI diesel vehicles were 75.1 %, 84.4 %, and 61.2 % lower, respectively. With a ~100 m increase in altitude, the EFs of PAHs, OPAHs, and NPAHs of diesel vehicles increased 1.88, 1.92, and 1.59 times due to incomplete combustion. In addition, the EFs of PAHs and OPAHs in gasoline vehicles were lower than those in diesel vehicles. In contrast, the proportion of PAHs with highly toxic components, such as dibenzo[a,h]anthracene (DahA) and benzo[a]pyrene (BaP), and the EFs of gas-phase NPAHs in gasoline vehicles were higher than those in diesel vehicles. Furthermore, the emissions of 1,8-DNP from diesel vehicles cannot be disregarded. 1,8-DNP was the main gas-phase NPAHs emitted by China VI and China V diesel vehicles, accounting for 49.3 % and 26.0 %, respectively. Moreover, gas-phase PACs contributed more to the EFs than particle-phase PACs, whereas particle-phase PACs have greater toxic effects. Although the EFs of PAHs are more than 100 times those of NPAHs, the toxic equivalent concentrations (TEQBaP) of PAHs in diesel and gasoline vehicles were approximately 6.5 times and 35 times those of NPAHs. The spatial distribution characteristics revealed that PACs emissions were mainly concentrated in urban areas and highways, and the differences in the toxicity of PACs emissions between different cities depended on the proportion of diesel vehicles. The average TEQBaP of PAHs and NPAHs in Haidong, Haibei, Huangnan, Hainan, Guoluo, and Yushu was 8.42 μg/m3 and 0.36 μg/m3, respectively, while those of Xining and Haixi were 0.24–0.29 μg/m3 and 0.09–0.108 μg/m3 higher, respectively. This study provides a comprehensive understanding of the emission characteristics, health risks, and spatial distribution of PACs from diesel and gasoline vehicle PACs in urban areas.

Spatial modelling of street-level carbon emissions with multi-source open data: A case study of Guangzhou

A refined spatial understanding of carbon emissions is crucial for advancing low-carbon development. This study aims to develop a comprehensive, open data-based approach for spatial modelling of carbon emissions at street level, covering five sectors: industry, transportation, residential & public service, commerce, and agriculture in Guangzhou. Two sets of open data, including statistical yearbook data and urban morphology data, were analyzed using a comprehensive methodology that integrates both bottom-up and top-down approaches to map the spatial distribution of carbon emissions. The findings delineate the carbon emission hierarchy across five distinct sectors as follows: Industrial (37.9%), Transportation (31.3%), Residential (18.6%), Commercial and Public Services (11.6%), and Agriculture (0.6%). The industrial sector emerges as the largest contributor, emitting 61.59 million tons, chiefly situated in suburban industrial zones like Huangpu and Panyu. Following closely is transportation, emitting 50.87 million tons, concentrated around Baiyun International Airport, ports, and urban areas with heavy traffic. Commercial and residential sectors emit 18.94 million tons, primarily within densely populated areas such as Tianhe and Haizhu. Agricultural emissions total 1.02 million tons, predominantly located on the city's outskirts, notably in Nansha. The findings of this study could provide information support for identifying carbon emission hotspots and developing sector-specific low-carbon urban planning strategies.

Dynamics and spatial distribution of black carbon emissions in the Murmansk Region

Dynamics and spatial distribution of black carbon emissions in the Murmansk Region

Spatial-Temporal Evolution and Influencing Factors of Animal Husbandry Carbon Emissions: A Case Study of Shandong Province, China

To further study the spatial distribution and dynamic evolution of carbon emissions from animal husbandry in Shandong Province, the panel data of 16 prefecture-level cities in Shandong Province from 2001 to 2022 were used to measure the carbon emissions of animal husbandry and the carbon emission intensity of animal husbandry. Based on the combination of space, kernel density estimation, and LMDI decomposition model, the spatial and temporal evolution of carbon emissions from animal husbandry in Shandong Province and its driving factors were investigated. The results show that: (1) The total amount of animal husbandry carbon emissions in Shandong Province showed a fluctuating downward trend, with a decrease of 10.10% during the investigation period, showing a peripheral-agglomeration distribution pattern. The carbon emission intensity showed a gradual downward trend, with an average annual decline of 7.47%, showing stepped distribution characteristics of high in the west and low in the east. (2) The difference in carbon emissions of animal husbandry among cities in Shandong Province increased first and then decreased, and the growth distribution was basically in the form of “bimodal”, showing a polarization pattern. (3) The intensity effect has the most obvious inhibitory effect on the carbon emission of animal husbandry; the effect of agricultural structure changes from a promoting effect to an inhibiting effect. The inhibitory effect of the industrial structure effect is second only to the intensity effect; the economic effect has the greatest promoting effect; and the promotion effect of the population size effect is small.

Quantifying high-resolution carbon emissions driven by land use change in the Guangdong-Hong Kong-Macao Greater Bay Area

Accurate research on carbon emission spatial distribution is vital for achieving carbon neutrality. Previous studies suffered from scale effects, demanding enhanced resolution. This study collected and computed land use carbon emission data for the Guangdong-Hong Kong-Macao Greater Bay Area (GBA). It also introduced a novel method to downscale this data into 30 m raster format using night light and NDVI data. The study showed a consistent annual rise in GBA land use carbon emissions, accompanied by increasing disparities. At the city level, emissions in the highest-ranking cities were 58 times higher than those in the lowest-ranking ones. Impervious land carbon emissions held over 90% sway over total carbon emissions, making them the primary driver of land use-related carbon emissions. Remarkably, high-emission zones, such as coastal cities Guangzhou, Foshan, Dongguan, and Hong Kong, expanded outward, forming multi-tiered, high-density carbon areas, giving rise to an “∩” shape pattern. Carbon emission change rates from cropland were modest, characterized by a pattern of fluctuation. Moreover, spatial autocorrelation and K-means cluster analyses uncovered a noticeable spatial correlation among urban land use carbon emissions in the GBA. Consequently, a compelling case exists for enhancing inter-city cooperation to collectively pursue regional emission reduction objectives.

The spatial effect of integrated economy on carbon emissions in the era of big data: a case study of China

The digital economy has the characteristics of resource conservation, which can solve China’s high carbon emissions problems. The digital economy can quickly integrate with the real economy, forming an integrated economy. However, it is still unclear whether an integrated economy can effectively reduce carbon emissions and achieve China’s ‘dual carbon goals’. Therefore, this study takes 30 provinces in China as the research object, constructs the integration economy index system through the statistical data from 2011-2021, and explores the spatial effect of the impact of the integration economy on carbon emissions by using principal component analysis, coupled coordination model and spatial econometric model. The research results are as follows. (1) From 2011 to 2021, the comprehensive economy showed a trend of increasing yearly (from 0.667 to 0.828), and carbon emissions showed a slow decrease (from 0.026 to 0.017). (2) Due to the infiltration of China’s economic development from the eastern to the western, the spatial distribution of the integrated economy shows a decreasing trend from east to west. The spatial distribution of carbon emissions may be related to China’s industrial layout of heavy industry in the northern, and light industry in the southern, showing a trend of low in the south and high in the north. (3) The integrated economy can significantly reduce carbon emissions (the coefficients of influence, -0.146), and the reduction effect will be more obvious if spatial spillover effects are taken into account (-0.305). (4) The eastern coast, the middle reaches of the Yangtze River, and the middle reaches of the Yellow River economic zones all increase carbon emissions at a certain level of significance (0.065, 0.148, and 3.890). The Northeast, South Coastal and Southwest economic zones significantly reduce carbon emissions (-0.220, -0.092, and -0.308). The results of the Northern Coast and Northwest are not significant (-0.022 and 0.095). (5) China should tailor regional economic development policies, such as strengthening investment in digital infrastructure in the Northwest Economic Zone and fully leveraging the spatial spillover effects of integrated economy in the Northeast, Southern Coastal, and Southwest Economic Zones to reduce carbon emissions.

Analyzing carbon emissions and influencing factors in Chengdu-Chongqing urban agglomeration counties

Majority of carbon emissions originate from fossil energy consumption, thus necessitating calculation and monitoring of carbon emissions from energy consumption. In this study, we utilized energy consumption data from Sichuan Province and Chongqing Municipality for the years 2000 to 2019 to estimate their statistical carbon emissions. We then employed nighttime light data to downscale and infer the spatial distribution of carbon emissions at the county level within the Chengdu-Chongqing urban agglomeration. Furthermore, we analyzed the spatial pattern of carbon emissions at the county level using the coefficient of variation and spatial autocorrelation, and we used the Geographically and Temporally Weighted Regression (GTWR) model to analyze the influencing factors of carbon emissions at this scale. The results of this study are as follows: (1) from 2000 to 2019, the overall carbon emissions in the Chengdu-Chongqing urban agglomeration showed an increasing trend followed by a decrease, with an average annual growth rate of 4.24%. However, in recent years, it has stabilized, and 2012 was the peak year for carbon emissions in the Chengdu-Chongqing urban agglomeration; (2) carbon emissions exhibited significant spatial clustering, with high-high clustering observed in the core urban areas of Chengdu and Chongqing and low-low clustering in the southern counties of the Chengdu-Chongqing urban agglomeration; (3) factors such as GDP, population (Pop), urbanization rate (Ur), and industrialization structure (Ic) all showed a significant influence on carbon emissions; (4) the spatial heterogeneity of each influencing factor was evident.

Research on Provincial Distribution of Total Carbon Emissions in the Power Generation Industry under Carbon Neutrality Constraints

The power generation industry in China is at the forefront of implementing carbon trading market policies, playing a crucial role in carbon emission reduction. Carbon allocation in each province is pivotal for future economic development and achieving a gradual and orderly carbon peak. By analyzing total carbon emissions from 2012 to 2021 and examining emission responsibilities from a producer perspective, this study extends the STIRPAT model to evaluate factors influencing carbon emissions in the national and provincial power generation sectors. Key findings highlight the urgent need for industrial structure changes in provinces like Xinjiang, Tibet, Guangdong, Shandong, and Jiangsu, which bear significant emission reduction responsibilities. The spatial and temporal distribution of carbon emissions is shifting from the southeast coast to central and western regions. Factors such as energy consumption, electric power intensity, urbanization rate, power structure, economic development level, and industrial composition significantly impact carbon emissions. To achieve carbon peak goals, a multifaceted approach is essential for reducing emissions in China's power generation sector, emphasizing the need for strategic interventions across various perspectives.

Drivers and spatial patterns of carbon emissions from residential buildings: An empirical analysis of Fuzhou city (China)

Global economic development will continue to harm the natural environment. China is the largest contributor to global carbon emissions, with cities and the construction sector playing a major role. Understanding carbon emissions from buildings in cities can inform mitigation measures. This study conducted a comprehensive accounting of carbon emissions from lighting and appliance electricity consumption in residential buildings. The investigation covered Fuzhou city in Fujian Province of China in 2011–2021. ArcGIS generated and analyzed the spatial patterns of carbon emissions by districts. The STIRPAT model identified the leading carbon-emission drivers. The findings indicate: (1) fine-grained spatial distribution of carbon emissions demonstrates a marked concentration in the city center and contiguous districts, low emissions in surrounding districts, and a gradually increasing trajectory; and (2) GDP per capita, urbanization rate, and resident population are the principal drivers of carbon emissions, with every 1 % increase in GDP per capita raising carbon emissions by 0.66 %. Relevant practical energy-saving and emission-reduction measures are distilled from the results. The findings provide a scientific basis for decision-makers to formulate emission-reduction targets and strategies for Fuzhou's residential buildings, a theoretical basis for promoting regional low-carbon development, and a reference for other regions with a similar developing economy.

Hybrid IFDMB/4D-Var inverse modeling to constrain the spatiotemporal distribution of CO and NO2 emissions using the CMAQ adjoint model

We performed a hybrid approach that combines the iterative finite difference mass balance (IFDMB) and four-dimensional variational data assimilation (4D-Var) methods to effectively constrain the spatiotemporal distribution of emissions. To quantitatively compare the performance of inverse modeling in constraining CO and NO2 emissions in South Korea spatiotemporally, we conducted a model-based twin experiment for three inverse modeling methods: IFDMB, 4D-Var, and hybrid inversions. We performed numerical modeling using the Community Multi-scale Air Quality (CMAQ) and its adjoints to calculate the values required for inverse modeling. As a result, the IFDMB inversion can effectively constrain the average spatial distribution of emissions. Meanwhile, the 4D-Var inversion can help estimate temporal variations in emissions, but it is not effective in regions with large prior emission errors. The hybrid inversion showed the best performance in constraining the spatiotemporal distribution of emissions because it combined the strengths of the two aforementioned methods. Furthermore, to compare the performance of the inverse modeling of pollutants with different chemical properties, we conducted additional inverse modeling for highly reactive NO2. After the application of inverse modeling, the emission errors of NO2 (18.339%) were larger than those of CO (10.593%). This difference in inverse modeling errors was due to the greater nonlinear relationship between emissions and concentrations in the inverse modeling process for NO2, which is more reactive compared to CO. In this study, the ideal modeling tests were performed to quantitatively assess the performance of inverse modeling. In future studies, we expect to apply the hybrid inversion approach used in this study to inverse modeling using actual observations.

Research on the Evaluation Method and Application of Provincial Differentiated Carbon Peaking in China

Promoting regions with favorable conditions to take the lead in reaching a carbon peak is an inevitable step towards achieving the dual carbon goals under the "nationwide coordinated action" plan. Considering the differences among Chinese provinces, this study measured the peaking pressure of each province based on the spatial distribution of carbon emissions. We then constructed a provincial peaking capacity evaluation system based on five dimensions, namely, peaking pressure, emission reduction status, economic development, policy support, and resource endowment, to comprehensively evaluate the carbon peaking capacity of 30 provincial administrative regions in China, excluding Hong Kong, Macau, Taiwan, and Tibet, using the entropy value method to determine the index weights. The 30 provinces were divided into five peaking tiers according to the evaluation results. The results showed that:① 18 regions, such as Hainan and Beijing, displayed a surplus in carbon emission space; eight regions, including Hebei and Shandong, showed a deficit in carbon emission space; and the carbon emission spaces allocated to Zhejiang, Anhui, Henan, and Hubei were comparable to their respective actual emissions. ② Developed regions generally had a higher carbon peaking capacity than that of less developed regions, with Beijing and Shanghai showing outstanding carbon peaking capacity, whereas Jiangxi and Guizhou had more room to improve their capacity. Finally, differentiated peaking targets and priority actions were proposed according to the provinces' different peaking tiers and local conditions.

Intelligent management of carbon emissions of urban domestic sewage based on the Internet of Things

Domestic wastewater is one of the major carbon sources that cannot be ignored by human society. Against the background of carbon peaking & carbon neutrality (Double Carbon) goals, the continuous urbanization has put heavy pressure on urban drainage systems. Nevertheless, the complex subjective and objective conditions of drainage systems restrict the field monitoring, measurement, and analysis of drainage systems, which has become a great obstacle to the study of carbon emissions from drainage system. In this paper, 3389 sensor terminals of Internet of Things (IoT) are used to build a field monitoring IoT for urban domestic wastewater methane (CH4) carbon emission, with 21 main districts of Chongqing Municipality in China as the study area. Incorporating Fick's law of diffusion, this field monitoring IoT derives a measurement model for methane carbon emissions based on measured concentrations, and solves the problems of long-term and stable monitoring and measurement of methane gas in complex underground environment. With GIS spatio-temporal analysis used to analyze the spatial and temporal evolution patterns of carbon emissions from septic tanks in drainage systems, it successfully reveals the spatial and temporal distribution of methane carbon emissions from drainage systems in different seasons, as well as the relationship between methane carbon emissions from drainage systems and the latitude of direct sunlight. Applying the DTW method, it quantifies the stability of methane monitoring in drainage systems and evaluates the effects of Sampling Frequency (SF) and Number of Devices Terminal (NDT) on the stability of methane monitoring. Consequently, an intelligent management system for carbon emissions from urban domestic wastewater is constructed on the base of IoT, which integrates methane monitoring, measurement and analysis in septic tanks of drainage systems.

Land use data can improve the accuracy of carbon emission spatial inversion model

AbstractInvestigating the spatial distribution and correlation characteristics of carbon emissions would be conducive to the policy formulation for precise carbon emission spatial reduction. Firstly, a new carbon emission spatial inversion model was developed, incorporating nighttime light data and land use data. After verifying the validity and accuracy of the inversion results, the continuous carbon emission spatial data in the Beijing‐Tianjin‐Hebei Urban Agglomeration (BTHUA) were acquired from 2000 to 2019. Then, the spatial distribution and correlation characteristics were further analyzed in the BTHUA. Finally, policy recommendations were proposed for carbon emission reduction and urban sustainable development. The results showed that the built model can improve the accuracy of the carbon emission spatial inversion data. The carbon emissions were low in the northwest and high in the southeast of the BTHUA, with a noticeable expansion of the high carbon emission contiguous areas around Beijing, Tianjin, Shijiazhuang, and other prefecture‐level cities, which was consistent with the socioeconomic development pattern. The center of gravity of carbon emissions moved to the southeast, showing a relatively stable distribution. The spatial correlation degree of carbon emissions among cities gradually increased, with Beijing and Tianjin playing a prominent role. As a scientific tool, the spatial inversion model helps to produce more accurate spatial data. The results and conclusions can provide useful and scientific references for spatial analysis and regulation strategies of regional carbon emission reduction.

Characterizing spatiotemporal dynamics of carbon emissions in peri-urban residential communities: A life cycle perspective

Building stock provides favorable material and welfare support to the urbanization process, but also triggers significant resource extraction and carbon emissions. Spatial explicit stock studies have focused mainly on developed areas and mature cities, with less attention paid to peri-urban areas. In terms of carbon emission, inconsistent system boundary selection and lack of building life cycle analysis limit the accuracy and comparability of results. Moreover, the absence of spatial distribution of emissions undermines support to regional carbon reduction initiatives. In this study, the Yangtze River Delta Integration Demonstration Area (YRDA), which is considered as a typical peri-urban area in China, was selected to study the spatiotemporal pattern of material flows and stocks at the residential community scale since the reform of the housing system in 1998, and to analyze spatiotemporal dynamics of carbon emissions over the life cycle of buildings. The results show that material inputs in peri-urban areas have experienced a fluctuating increase, with a gradual decline after 2016 as housing policies were tightened, and have a lower residential stock intensity than city centers. Residential carbon emissions also fluctuate while trending upwards, with operation accounting for the highest share of emissions, followed by building material production, with demolition now causing a rapid growth in emissions. Material stocks and residential carbon emissions display significant spatial heterogeneity, and a new concentration area has formed at the intersection of the three administrative districts that of YRDA. The study underscores the influence of economic growth and housing policy on the spatiotemporal pattern of building stocks, flows and carbon emissions, offering insights for cross-boundary ecosystem governance and regional circular economy development.

AgriFireInfo v1.0: An Open-Source Platform for the Monitoring and Management of Open-Field Crop Residue Burning

Open-field crop residue burning (OCRB) is a widespread agricultural practice with significant impacts on regional environments and public health. The effective management of OCRB remains a challenging task that requires timely access to various forms of monitored and forecasted information. Addressing this worldwide need, an open-source platform named AgriFireInfo v1.0, which is specifically tailored to monitoring and regulating regional OCRB activities, was developed. This technical note thoroughly illustrates the platform’s architecture, major modules, and visualization processes. Through AgriFireInfo v1.0, government agencies can access timely information about the spatial distribution of fire spots and emissions as well as meteorological conditions and air quality status. AgriFireInfo v1.0 also introduces an innovative Prevention Alarming Index, designed to identify regions prone to OCRB and promote comprehensive crop residue utilization. Furthermore, it offers the burning window and crop residue yields for controlled OCRB activities and can be used to analyze shifts in farmers’ burning behaviors and intensities. Future enhancements will focus on supplying holistic information on the burning windows and burning amounts of crop residues to further facilitate refined controlled burning activities and optimize decision-making processes. The flexibility and scalability of this platform can potentially allow users to easily customize and apply it to other regions or countries.

Can Financial Agglomeration Development Reduce Carbon Emissions? Evidence from the Yangtze River Delta Region of China

Reducing carbon emissions is a crucial measure for achieving sustainable development. The financial industry exhibits remarkable spatial agglomeration characteristics, which play a pivotal role in advancing carbon emission reduction and facilitating energy transformation. Using panel data from 41 cities in the Yangtze River Delta from 2008 to 2019, this study employed a spatial econometrics model to investigate the impacts and spatial spillover effects from the development of financial agglomeration on carbon emissions and the associated underlying mechanisms. The research shows that (1) there is an uneven spatial distribution of carbon emissions and financial development within the Yangtze River Delta region of China; (2) an inverted U-shaped relationship exists between financial agglomeration and carbon emissions, and only Shanghai’s level of financial agglomeration exceeds the extremum point; (3) financial agglomeration shows a negative spatial spillover effect on carbon emissions; and (4) financial agglomeration can promote industrial movement toward reducing carbon emissions. The study suggests some strategies for carbon reduction in China.