The carbon emissions related to the land-use changes from 2000 to 2015 in Shenzhen, China: Implication for exploring low-carbon development in megacities

Land-use simulation for synergistic pollution and carbon reduction: Scenario analysis and policy implications

Simulations of sustainable land use and management are required to achieve targets to reduce pollution and carbon emissions. Limited research has been conducted on synergistic pollution and carbon reduction (SPCR) in land-use simulations. This study proposed a framework for land-use simulation focused on SPCR. The non-dominated sorting genetic algorithm (NSGA-Ⅱ) and the entropy weight-based technique for order of preference by similarity to an ideal solution (TOPSIS) were used to optimize the land-use structure according to minimum net carbon, nitrogen, and phosphorus emissions. The cellular automata (CA) Markov model was then utilized to simulate the land-use spatial pattern according to the optimal conditions. The proposed framework was applied to the Dongjiang River Basin, South China, and three other scenarios (natural development (ND), carbon minimization (CM), and pollution minimization (PM)) were designed to validate the effectiveness of pollution and carbon emissions reduction under the SPCR scenario. The land-use structure and the pollution and carbon emissions in the scenarios were compared. The results showed the following. (1) The proportions of cultivated land, woodland, grassland, water, and construction land In the SPCR scenario accounted for 14%, 72%, 4%, 3%, and 7% of the total area, respectively. The carbon, nitrogen, and phosphorus emissions were 42.4%, 6.6%, and 7.8% lower, respectively, in the SPCR scenario than in the ND scenario, demonstrating the advantages of simultaneous pollution and carbon reduction. (2) The kappa coefficient of the CA-Markov model was 0.8729, indicating high simulation accuracy. (3) The simulated land-use spatial patterns exhibited low spatial heterogeneity under the CM, PM, and SPCR scenarios. However, there were significant disparities between the ND and SPCR scenarios. The cultivated and construction land areas were significantly smaller in the SPCR scenario than in the ND scenario. In contrast, the woodland and grassland areas were larger, with most differences in the central and southwestern regions of the Dongjiang River Basin. The results of the current study can be used to formulate effective land use policies and strategies in the Dongjiang Basin and similar areas to achieve the Coupling coordination between pollution reduction and carbon reduction. Policy recommendations include increasing the proportion of woodland and grassland, implementing reasonable constraints on expanding cultivated and construction lands, and establishing farmland red lines to promote synergistic pollution and carbon reduction.

High-carbon expansion or low-carbon intensive and mixed land-use? Recent observations from megacities in developing countries: A case study of Shanghai, China

Cities have become significant sources of greenhouse gas emissions. Effective land management may be the solution to carbon neutrality targets for megacities with limited land resources. This paper takes Shanghai as a case study to investigate the regional land use dynamics and its impact on carbon emissions following the implementation of land conservation and intensive use policy. During 2010-2020, the land use pattern in Shanghai changed from the previous urban land expansion to a combination of industrial land reduction and woodland expansion. Meanwhile, the area proportion of land-use mixture grids increased from 90.50% to 92.28% with the spatial pattern of mixed types also changing. Furthermore, the notable land-use mixture does not necessarily lead to carbon emission reduction, but it can reduce carbon emission hotspots in industrial agglomerations by promoting the mixed use of industrial land and other land use types. However, megacities cannot achieve carbon balance through land use management alone. Due to the increasing carbon emission density of hybrid industrial land, the joint implementation of a land conservation and intensive use strategy with industrial and energy structure adjustments may be an effective way forward.

Impact of urban spatial structure elements on carbon emissions efficiency in growing megacities: the case of Chengdu

Quantitative research on the impact weight and impact of regional heterogeneity of urban spatial structure elements on carbon emissions efficiency can provide a scientific basis and practical guidance for low-carbon and sustainable urban development. This study uses the megacity of Chengdu as an example to measure and analyze the spatial carbon emission efficiency and multidimensional spatial structure elements by building a high-resolution grid and identifying the main spatial structure elements that affect urban carbon emissions and their impact weights via the Ordinary Least Squares regression (OLS) and Geographically Weighted Regression (GWR). The spatial heterogeneity of the impact of each element is also explored. The results show that the overall carbon emission efficiency of Chengdu is high in the center and low on the sides, which is related to urban density, functional mix, land use, and traffic structure. However, the influence of each spatial structure element is different in the developed central areas, developing areas of the plain, mountainous developing areas, underdeveloped areas of the plain, and mountainous underdeveloped areas. Thus, it is appropriate to form differentiated urban planning strategies based on the characteristics of the development of each zone. The findings provide inspiration and a scientific basis for formulating policies and practice to the future low-carbon development of Chengdu, while provide a reference for other growing megacities.

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.

Differential characteristics of carbon emission efficiency and coordinated emission reduction pathways under different stages of economic development: Evidence from the Yangtze River Delta, China

Regional carbon emission efficiency (CEE) has differentiated characteristics under different economic development stages and patterns, and identifying such characteristics is important for formulating corresponding policies for high-quality regional development. Using input‒output data related to economic development and energy consumption, a comprehensive evaluation model of the Super-SBM and Malmquist‒Luenberger (ML) index is constructed to evaluate the spatial and temporal changes and driving forces of CEE. Based on this index, a proposal is designed for collaborative carbon emission reduction zoning. The results indicate that the CEE of the Yangtze River Delta shows a fluctuating upward trend with obvious spatial agglomeration characteristics, and CEE changes are closely related to economic development stages. The annual average CEE values in each stage show positive changes, indicating that economic development gradually evolves to low carbonization levels. Moreover, CEE improvement gradually shifts from being driven by efficiency changes to being driven by technological changes. Finally, according to the characteristics of total carbon emissions and the efficiency of different cities, a synergistic emission reduction path is proposed with four aspects: land use optimization, ecological co-preservation, innovation cooperation and low carbon development.

Monitoring spatiotemporal characteristics of land-use carbon emissions and their driving mechanisms in the Yellow River Delta: A grid-scale analysis

Comprehensive and accurate grasp of land-use carbon emissions (LCE) level and its driving mechanism is key to success in China's pursuit of low-carbon development, and it is also the scientific basis for the formulation and implementation of regional carbon emissions strategies. Based on fossil fuel carbon emissions raster data (published by the Open-Data Inventory for Anthropogenic Carbon dioxide (ODIAC) platform) and land use data, this manuscript selects the Yellow River Delta as the study area and uses an improved LCE measurement model, exploratory spatial data analysis, multiscale geographical weighting regression (MGWR), and other models to explore the spatiotemporal heterogeneity and driving mechanisms of LCE at the grid level. The results showed the following: ① The total amount of LCE in the study area continued to increase from 2000 to 2019, the growth rate decreased, but the peak of LCE had not yet been reached. ② The LCE of the study area showed a significant positive global autocorrelation. The H-H aggregation region showed a relatively stable spatial distribution range; the L-L aggregation region showed wide distribution characteristics that covered the entire study area; and the aggregation regions of H-L and L-H, which have not yet reached the scale. ③ At the global dimension, the mean correlation coefficients between LCE and driving factors (net primary productivity (NPP), nighttime light (NTL), and population density (PD)) from 2000 to 2019 were -0.11, 0.28, and 0.12; at the local dimension, the strength (from strong to weak) of the effect of each factor on LCE was PD, NTL, NPP (2000) and NTL, PD, NPP (2019). The research results provide a scientific basis and basic guarantee for the development, and implementation of regional carbon emission strategies.

Optimization of Spatial Land Use Patterns with Low Carbon Target: A Case Study of Sanmenxia, China

Land use change is an important factor in atmospheric carbon emissions. Most of the existing studies focus on modeling the land use pattern for a certain period of time in the future and calculating and analyzing carbon emissions. However, few studies have optimized the spatial pattern of land use from the perspective of the impact of carbon emission constraints on land use structure. Therefore, in this study, the effects of land use change on carbon emissions from 1990 to 2020 were modeled using a carbon flow model for Sanmenxia, Henan, China, as an example. Then, the land use carbon emission function under the low carbon target was constructed, and the differential evolution (DE) algorithm was used to obtain the optimized land use quantity structure. Finally, the PLUS model was used to predict the optimal spatial configuration of land use patterns to minimize carbon emissions. The study produced three major results. (1) From 1990 to 2020, the structural change of land use in Sanmenxia mainly occurred between cultivated land, forest land, grassland and construction land. During this period of land use change, the carbon emissions from construction land first increased and then decreased, but despite the decrease, carbon emissions still exceeded carbon sinks, and the carbon metabolism of land use was still far from equilibrium. (2) Between 2010 and 2020, the area of cultivated land began to decrease, and the area of forest land rapidly increased, and land-use-related carbon emissions showed negative growth. This showed that the structural adjustment of energy consumption in Sanmenxia during the period decreased carbon emissions in comparison with the previous period. (3) A comparison of predicted optimized land use patterns with land use patterns in an as-is development scenario showed a decrease in construction land area of 23.05 km² in 2030 with a steady increase in forest land area and a decrease in total carbon emission of 20.43 t. The newly converted construction land in the optimized land use pattern was concentrated in the ribbon-clustered towns built during urban expansion along the Shaanling basin of the Yellow River and the Mianchi-Yima industrial development area.