Identifying the spatial heterogeneity in the effects of the construction land scale on carbon emissions: Case study of the Yangtze River Economic Belt, China

How changes in landscape patterns affect the carbon emission: a case study in the Chengdu-Chongqing Economic Circle, China

The construction of low-carbon cities is an optimal means to balance the competing interests of economic growth and carbon emission reduction. This study focuses on the optimization of land use patterns with a low carbon orientation, taking the Chengdu-Chongqing Economic Circle (CCEC), the fourth-largest economic growth pole in China, as an example. The panel data regression analysis is carried out to identify the dynamic correlations between the landscape changes and the carbon emission induced by land use and land cover change (LICE) of each city, each year, for the last 20 years. The results show that the CCEC has witnessed a 142.85% increase in carbon emissions during the period studied, with the growth of built-up land contributing 94% of total carbon emissions from 2000 to 2020. By constructing the panel regression model, this study finds that the intensity of carbon emissions increases significantly as the urban built-up land area and the agglomeration of artificial structures increase. The conversion of cropland, which dominates the landscape pattern, to built-up land has led to further fragmentation of the landscape pattern and a reduction in LPI, thus increasing carbon emissions. And a more complex regional landscape pattern will have a positive impact on carbon emission reduction. Based on the above findings, suggestions are articulated for carbon emission reduction.

Spillover effects of urban form on urban land use efficiency: evidence from a comparison between the Yangtze and Yellow Rivers of China

The contradiction between the basin's economic importance and its role as an ecological barrier impedes efficient urban land use. This study aims to propose an integrated approach to compare the urban land use of two representative basin areas of the Yangtze River Economic Belt and the Yellow River Basin and to investigate the impact of urban form on urban land use efficiency. Urban form was characterized by landscape indexes including Patch Density, Largest Patch Index, Edge Density, Patch Cohesion Index, and Agglomeration Index based on FRAGSTATS 4.0 software, and urban land use efficiency was measured by using Slack-Based Model-Undesirable, considering urban land becomes an emission source. Furthermore, spatial econometric models were adopted to explore direct effects and spatial spillover effects of urban form on urban land use efficiency. From 2000 to 2018, changes in urban form in both Yangtze River Economic Belt and Yellow River Basin showed increased fragmentation, enhanced heterogeneity, and more complex patch shapes. The high values of urban land use efficiency were concentrated in lower reaches of the Yangtze and Yellow Rivers. Spatial econometric models suggested that between different basins and various sized cities, the impact of urban form on urban land use efficiency had a spatial spillover effect and regional heterogeneity. Results indicated that input factors such as capital and labor should be more concentrated in metropolitan areas and urban agglomerations, thus promoting higher land use efficiency.

Prediction of carbon emissions peak and carbon neutrality based on life cycle CO2 emissions in megacity building sector: Dynamic scenario simulations of Beijing

In order to design an optimal carbon peak and carbon neutralization pathway for the high-density building sector, a dynamic prediction model is established using system-dynamics coupled building life cycle carbon emission model (SD-BLCA) with consideration of future evolutionary trajectory and time constraints. The model is applied in Beijing using the SD-BLCA combined with scenario analysis and Monte Carlo methods to explore optimal trajectory for its building sector under 30-year timeframe. The results indicate that by increasing the proportion of renewable energy generation by 7% and retrofitting 60 million m2 of existing buildings, these two mature measures can offset the growth of carbon emissions and achieve the peak target by 2025. However, achieving carbon neutrality necessitates a shift from isolated technologies to a comprehensive net-zero emissions strategy. The study proposes a time roadmap that integrates a zero-carbon energy supply system and the carbon reduction measures of the whole life cycle. This strategy primarily relies on renewable sources to provide heat, power, and hydrogen, resulting in estimated reductions of 29.8 Mt, 28.1 Mt, and 0.7 Mt, respectively. Zero energy buildings, green buildings, and renovated buildings can reduce carbon emissions through their own energy-saving measures by 8.4, 18.2, and 11.8 kg/m2, respectively.

Impact of the intensive use of urban construction land on carbon emission efficiency: evidence from the urban agglomeration in the middle reaches of the Yangtze River

Urban construction land, as the main carrier of socioeconomic activities, is also a land type that is associated with large carbon emissions. This study uses statistical data of the urban agglomeration in the middle reaches of the Yangtze River (UAMRYR) from 2006 to 2020 to examine the mechanism of the intensive use of urban construction land (IUUCL) on carbon emission efficiency (CEE) from the perspective of urban land resource utilization. The study shows that the capital-intensive and technology-intensive use of urban construction land can significantly increase CEE, while increased labor and energy intensification inhibits CEE. In addition, there is regional heterogeneity in the effect of the IUUCL on CEE. The external control factor industrial structure has the most obvious inhibiting effect on the CEE of the Wuhan urban circle, the intensive use of energy has become the crucial constraint on the carbon emission reduction of the city cluster around Poyang Lake, and the intensive use of science and technology is the key factor in realizing the green and low-carbon development of the Chang-Zhu-Tan city cluster. This study innovatively constructs a theoretical framework of IUUCL versus CEE and conducts a heterogeneous study on the CEE of intensive use of construction land from the perspective of urban agglomerations. By providing a better understanding of the intrinsic influence mechanism of both these processes, this study provides a new perspective for reducing carbon emissions.

Driving impact and spatial effect of the digital economy development on carbon emissions in typical cities: a case study of Zhejiang, China

The digital economy (DE) not only drives economic innovation and development but also has significant environmental effects by promoting lower carbon emissions. To investigate the spatial effects of DE on urban carbon emissions, this study comprehensively measures the level of DE development based on the panel data from 11 typical cities in Zhejiang Province from 2011 to 2020, by comparing analysis using different regression models. The following conclusions are obtained: (1) The total carbon emissions (TC) of Zhejiang cities in general show a fluctuating change trend of first increasing and then slowly decreasing, while carbon emission intensity and carbon emission per capita in general show a fluctuating change trend of decreasing. Cities with high TC are primarily concentrated in the Hangzhou Bay city cluster, accounted for 62 ~ 65% of the province's carbon emissions. The development of the DE in Zhejiang cities shows steady growth, but there are large differences among cities, with Hangzhou and Ningbo standing out as particularly prominent. (2) There is a significant inverted U-shaped relationship between the DE and the level of carbon emissions in Zhejiang Province. The influence coefficient of the DE on the primary term of TC is 0.613, and the influence coefficient of the quadratic term of TC is - 1.008. (3) In terms of the spatial spillover effect of the DE on carbon emissions, the study finds that compared to the direct effect, the spatial spillover effect is not significant. However, the allocation of transport resources shows a positive spatial spillover effect (increasing carbon emissions, coefficient value is 0.138), while technological progress shows a somewhat negative spatial spillover effect (decreasing carbon emissions, coefficient value is - 0.035). (4) The study also finds that the smart city pilot policy significantly reduces urban carbon emissions. Moreover, the effect of the DE on carbon emissions is confirmed through the significance test of the quadratic term when replacing the geographical and economic distance weight matrices. This indicates that the empirical findings are robust to these tests. Finally, several countermeasures to reduce carbon emissions are proposed from the perspective of DE development.

Spatial-temporal dynamics of land use carbon emissions and drivers in 20 urban agglomerations in China from 1990 to 2019

Urban agglomerations (UAs) are the largest carbon emitters; thus, the emissions must be controlled to achieve carbon peak and carbon neutrality. We use long time series land-use and energy consumption data to estimate the carbon emissions in UAs. The standard deviational ellipse (SDE) and spatial autocorrelation analysis are used to reveal the spatiotemporal evolution of carbon emissions, and the geodetector, geographically and temporally weighted regression (GTWR), and boosted regression trees (BRTs) are used to analyze the driving factors. The results show the following: (1) Construction land and forest land are the main carbon sources and sinks, accounting for 93% and 94% of the total carbon sources and sinks, respectively. (2) The total carbon emissions of different UAs differ substantially, showing a spatial pattern of high emissions in the east and north and low emissions in the west and south. The carbon emissions of all UAs increase over time, with faster growth in UAs with lower carbon emissions. (3) The center of gravity of carbon emissions shifts to the south (except for North China, where it shifts to the west), and carbon emissions in UAs show a positive spatial correlation, with a predominantly high-high and low-low spatial aggregation pattern. (4) Population, GDP, and the annual number of cabs are the main factors influencing carbon emissions in most UAs, whereas other factors show significant differences. Most exhibit an increasing trend over time in their impact on carbon emissions. In general, China still faces substantial challenges in achieving the dual carbon goal. The carbon control measures of different UAs should be targeted in terms of energy utilization, green and low-carbon production, and consumption modes to achieve the low-carbon and green development goals of the United Nations' sustainable cities and beautiful China's urban construction as soon as possible.

Future carbon storages of ecosystem based on land use change and carbon sequestration practices in a large economic belt

Assessments of ecosystem carbon storage are needed to form the scientific basis for carbon policies. Due to lack of data, there are few accurate, large-scale, and long-term predictions of ecosystem carbon storage. This study used the Distributed Land-Use Change Prediction (DLUCP) model with ten socioeconomic and two climate change scenarios for a total of 20 combinations that take into account population increase, technology innovation, climate change, and Grain for Green Project to make high-resolution predictions of land use change in the Yangtze River Economic Belt. Low and high carbon sequestration practices were considered to predict future carbon densities. Land use change data, carbon densities data, and the InVEST model were used to predict changes in ecosystem carbon storage from now to 2070. The results show a slight increase (1.88-4.17%) in carbon storage in the study area only based on land use change. Grain for Green Project has the largest impact on carbon storage among population increase, technology innovation, climate scenarios, and Grain for Green Project, which increases carbon storage by 4.17%. After the implementation of carbon sequestration practices, there is an increase in carbon storages from 28.51 to 56.77% in the study area from now to 2070, and increasing carbon storages of forest in each stream and carbon storage of cropland in downstream are efficient ways to achieve carbon neutralization.

What were the spatiotemporal evolution characteristics and the influencing factors of urban land green use efficiency? A case study of the Yangtze River Economic Belt

China's rapid urbanization has had a tremendous impact on the country's limited land resources, and one of the major issues of green development is how to utilize the limited land resources to maximize social, economic, and environmental advantages. From 2005 to 2019, the super epsilon-based measure model (EBM) was employed to assess the green land use efficiency of 108 prefecture-level and above cities in the Yangtze River Economic Belt (YREB), as well as investigate its spatial and temporal evolution and influential factors. The findings demonstrate that overall, urban land green use efficiency (ULGUE) in the YREB has been ineffective; in terms of city scale, megacities have the highest efficiency, followed by large cities and small and medium-sized cities; and at the regional level, downstream efficiency does have the greatest average value, followed by upstream efficiency and middle efficiency. The results of temporal and spatial evolution reveal that the number of cities with a high ULGUE is increasing in general but that their spatial characteristics are relatively dispersed. Population density, environmental regulation, industrial structure, technology input, and the intensity of urban land investment all have major beneficial effects on ULGUE, whereas urban economic development level and urban land use scale clearly have inhibitory effects. In light of the previous conclusions, some recommendations are made to continuously improve ULGUE.

Effects of land-use change on carbon emission and its driving factors in Shaanxi Province from 2000 to 2020

Exploring the process of carbon emissions under the "carbon peaking and carbon neutrality goals" can contribute to sustainable economic development. This research takes Shaanxi Province as an example. We elaborated on the spatial and temporal characteristics of land-use change from 2000 to 2020 and adopted the carbon emission model method to calculate land-use carbon emissions, also used urban morphological indicators to reveal the main factors of carbon emission changes. The results show that from 2000 to 2020, the land-use change in Shaanxi Province is mainly reflected in the increase in construction land area and the decrease in agricultural land area. Among them, the construction land area increased by 2192 km², and the agricultural land area decreased by 5006 km². Land-use carbon emissions increased by 1.28 × 10¹¹ kg during this period. Construction land is a major contributor to carbon emissions. The forestland is the main carbon sink. Carbon emissions showed a spatial pattern of "high in the north, low in the south, and concentrated in the middle." Urban form change is the driving factor affecting land-use carbon emissions in Shaanxi Province. The results of the research contribute to the understanding of regional carbon emission mechanisms and provide a scientific basis for reducing carbon emissions.

The effects of urban land use on energy-related CO2 emissions in China

Land use change caused by urbanization is widely believed to be the primary way human activities affect energy use and, thus, CO₂ emissions (CEs) in China. However, there is a limited understanding of the role of land use with detailed categories in energy-related CEs is still absent. This paper aims to narrow the knowledge gap using multi-dimension metrics, including land use scale, mixture, and intensity. These metrics were derived from three years of sequential POI data. A GWR analysis was carried out to examine the associations between land use change and energy-related CEs. Our results show that (1) the scale of most land use types exerted a bidirectional effect on CEs, demonstrating apparent spatiotemporal heterogeneity; (2) land use mixture of mature city agglomerations had a significant suppressive effect on CEs, suggesting mixed land use be advocated in the urbanization process; (3) Land use intensity had a bi-directional association with CEs in most cities, but its adverse effect gradually spread from the west to the northeast. Therefore, systematically regulating land transaction to control land scale, appropriately interplanting biofuel plants, and utilizing renewable energy are encouraged to reduce energy footprints and mitigate CEs in China. The findings and conclusions of this paper enhance our knowledge on the relationship between land use and CEs and present the scientific basis for policy-making in building low-carbon cities in the context of rapidly urbanizing China.

Exploring the effect of ecological land structure on PM2.5: A panel data study based on 277 prefecture-level cities in China

In the context of serious urban air pollution and limited land resources, it is important to understand the environmental value of ecological land. Previous studies focused mostly on the effectiveness of a particular type of green space or the total amount of ecological land on PM_2.5 and have rarely analyzed the association between ecological land structure and PM_2.5 systematically and quantitatively. Therefore, we took 277 cities in China as an example, comprehensively compared the results of different models, and selected a spatial Durbin model using time-fixed effects to dissect the degree of influence of ecological land and different land types within it on PM_2.5. The urban ecological land use structure was closely related to PM_2.5, and the higher the proportion of ecological land use was, the lower the PM_2.5. The degree and direction of influence of different types of land functions within ecological land on PM_2.5 differed, with forests, shrubs, and grasslands causing a weakening impact on PM_2.5, while wetlands and waters did not have a weakening role. The degree of reduction of PM_2.5 by a single type of ecological land was significantly smaller than that by a composite type of ecological land. Green space should be comprehensively considered, designed and adjusted in urban planning to continuously optimize the ecological spatial structure, increase landscape diversity and maximize ecological benefits. The findings of this study help with exploring the effects of land use structure under the goal-oriented control of air pollution and provide theoretical reference and decision-making support for formulating precise air pollution control policies and optimizing the spatial development of national land.

Spatially explicit carbon emissions by remote sensing and social sensing

Scientific simulation of carbon emissions is an important prerequisite for achieving low-carbon green development and carbon peak and carbon neutralization. This study proposed a carbon emissions spatialization method based on nighttime light (NTL) remote sensing and municipal electricity social sensing. First, the economics-energy comprehensive index (EECI) was proposed by integrating the NTL and municipal electricity consumption (EC) data. Second, the carbon emissions were spatialized at a fine scale based on NTL, EC, and EECI, respectively. Finally, the geographical detector model was applied to quantify the influencing factors on carbon emissions from the perspectives of individuals and interactions. Results show that combining remote sensing and social sensing data helps depict carbon emissions accurately. The factor analysis found that GDP and population were the basis of carbon emissions, while the secondary industry and urbanization rate were the direct factors. This study is expected to provide constructive suggestions and methods for emission reduction, carbon peak, and carbon neutrality in high-density cities in China.

Analysis of Dynamic Evolution and Spatial-Temporal Heterogeneity of Carbon Emissions at County Level along "The Belt and Road"-A Case Study of Northwest China

Northwest region is the main energy supply and consumption area in China. Scientifically estimating carbon emissions (CE) at the county level and analyzing the spatial-temporal characteristics and influencing factors of CE in a long time series are of great significance for formulating targeted CE reduction plans. In this paper, Landscan data are used to assist NPP-VIIRS-like data to simulate the CE from 2001 to 2019. Spatial-temporal heterogeneity of CE was analyzed by using a two-stage nested Theil index and geographically and temporally weighted regression model (GTWR). The CE in northwest China at the county increases yearly while the growth rate slows down from 2001 to 2019. The spatial pattern forms a circle expansion centered on the high-value areas represented by the provincial capital, which is also obvious at the border between Shaanxi and Ningxia. Axial expansion along the Hexi Corridor is conspicuous. The spatial pattern of CE conforms to the Pareto principle; the spatial correlation of CE in northwest counties is increasing year by year, and the high-high agglomeration areas are expanding continuously. It is an obvious high carbon spillover effect. Restricted by the ecological environment, the southwest of Qinghai and the Qinling-Daba Mountain area are stable low-low agglomeration areas. The spatial pattern of CE in northwest China shows remarkable spatial heterogeneity. The difference within regions is greater than that between regions. The "convergence within groups and divergence between groups" changing trend is obvious. According to the five-year socioeconomic indicators, the economic scale (GDP), population scale (POP), and urbanization level (UR) are the main influencing factors. The direction and intensity of the effect have changed in time and space. The same factor shows different action intensities in different regions.

The Forms, Channels and Conditions of Regional Agricultural Carbon Emission Reduction Interaction: A Provincial Perspective in China

Agricultural emission reduction is a key objective associated with sustainable agricultural development and a meaningful way to slow down global warming. Based on the comprehensive estimation of agricultural carbon emissions, this study applied the traditional spatial Durbin model (SDM) to analyze the type of regional emission reduction interaction and explore whether it is a direct or an indirect interaction caused by technology spillovers. Moreover, geographic, economic, and technical weights were used to discuss the channels of emission reduction interactions. The partitioned spatial Durbin model was applied to explore the realization conditions of regional emission reduction interactions. We found that: (1) comprehensive emission reduction interactions were identified in various regions of China, including direct and indirect interactions, in which geographic and technical channels were the major pathways for direct and indirect emission reduction interactions, respectively; (2) regions with similar economic development levels are more likely to have direct interactions, whereas regions with low technical levels are more willing to follow the high-tech regions, and the benchmarking effect is noticeable; (3) emission reduction results promoted by economic cooperation may be offset by vicious economic competition between regions, and more emission reduction intervention measures should be given to regions with high economic development levels; (4) to achieve better technological cooperation, regions must have similar technology absorption capabilities and should provide full play to the driving force of technical benchmarks.

Has Urban Construction Land Achieved Low-Carbon Sustainable Development? A Case Study of North China Plain, China

The rapid expansion of urban construction land (UCL) provides a guarantee to support rapid economic development and meet the social needs of urban residents. However, urban construction land is also an important source of carbon dioxide emissions. Therefore, it is of great research value to investigate the relationship between UCL and carbon emissions in depth. Based on this, using panel data of 57 cities in the North China Plain from 2007 to 2018, the study found that there is a strong positive correlation between UCL and CO2 emissions. It can be seen that the expansion of UCL is an important source of CO2 emissions. On the basis of this research conclusion, first, this paper uses the Tapio decoupling model to analyze the decoupling relationship between UCL and carbon emissions in the North China Plain. Then, the spatial autocorrelation analysis was applied to explore the spatial correlation characteristics of the carbon emission intensity of UCL in cities in the North China Plain. Finally, using the GTWR model to analyze the influencing factors of the carbon emission intensity of UCL, the following conclusions were drawn. In 2007–2015, the decoupling relationship performed well, but it deteriorated significantly from 2015 to 2018; in addition, there was a significant positive spatial correlation of carbon emission intensity of UCL. Various influencing factors have a significant impact on the carbon emission intensity of UCL, for example, the urbanization rate, industrial structure, economic development level, and population density have a positive impact, and environmental regulations, foreign investment intensity, land use efficiency and greenery coverage have a negative impact. The research results of this paper provide a scientific basis for making decisions and optimizing pathways to achieve carbon emission reduction from UCL in the North China Plain, as well as certain reference values for other regions to achieve low-carbon development of UCL. This is significant for exploring the optimal solution of land and carbon emissions and building a harmonious human–land relationship.