Spatial Optimization of Land Use Pattern toward Carbon Mitigation Targets—A Study in Guangzhou

Abstract

Global climate change is one of the major challenges facing the world, and the spatial optimization of land use patterns has been regarded as critical in realizing carbon mitigation. In this study, the linear programming model and the Markov Chain model are integrated in different scenarios to optimize land use structure for low-carbon development. The land use pattern is then simulated through the adjusted convolutional neural network and cellular automata model, taking Guangzhou City as the case study area. The results reveal that construction land with high economic efficiency will increase its area, and the reaming types will experience slight changes, in 2035 in the natural development scenario and the economic priority scenario. Ecological land such as forest land, grassland, and water is partly occupied by construction land in the urban–rural fringe areas. The total carbon emissions decrease by 2.32% and 1.57% in these two scenarios. In the low-carbon-oriented scenario, the expansion of construction land is restricted, and the forest land and grassland undergo great expansion. The total carbon emission decreases by 18.95%—a figure much larger than that in the natural development scenario and the economic priority scenario. Our paper embeds the needs and constraints in land spatial planning into the spatial optimization of the land use pattern, which provides valuable references for low-carbon city development in the future.

Spatial Optimization of Land Use Allocation Based on the Trade-off of Carbon Mitigation and Economic Benefits: A Study in Tianshan North Slope Urban Agglomeration

“…The results of this paper have both similarities and differences with the existing literature. In terms of simulating the spatial allocation of land in 2030, compared with 2020, the land pattern changes significantly under the two targets, and the low-carbon target brings about a more significant reduction in carbon emissions compared with the economic target and the natural development target, which is consistent with the results of the studies by Ding et al [5,55] and Zeng et al [25]. The values of total economic benefits and total carbon emissions are the largest in the economic development scenario of the economic targets and the smallest in the low-carbon development scenario of the low-carbon targets, which again verifies the trade-offs between economic development and carbon emissions, which is consistent with the results of the studies by Li et al [56] and You et al [27].…”

“…The average temperature of the Earth's surface has risen in the last one hundred years above the highest levels of the previous few thousand years and is expected to continue to rise [1][2][3]. The increase in the average temperature has led to a series of climatic and ecological problems, such as the occurrence of extreme weather events (droughts and floods), the rise in sea level, and the decrease in biodiversity; global warming has become one of the severe challenges to the world's economic development [4][5][6]. In response to climate warming, the Paris Agreement proposes that global warming within the 21st century should be kept within 2 • C compared to pre-industrial levels, and efforts should be made to keep it within 1.5 • C. Otherwise, the ecological environment will continue to deteriorate [7,8].…”

“…Land use spatial optimization is based on the human-land relationship [15], system theory [16], ecological economy [17], spatial equilibrium theory [18], etc. and adjusts the structure and layout of land use types by setting objectives and constraints to achieve the benign development of society, economy, and ecology [5,19,20]. Based on different conflicting goals, the early literature focused on the ecological service system and economic benefits as objectives to simulate land demand [21,22]; with the gradual maturation of social benefit measurement methods [23], social benefits were also introduced, such as Zhao et al setting up the maximization of ecological, economic, and social benefits and adopting the principles of land use suitability and spatial compactness to optimize the land structure and space [24].…”

Spatiotemporal Effects and Optimization Strategies of Land-Use Carbon Emissions at the County Scale: A Case Study of Shaanxi Province, China

“…Building upon carbon emission accounting, scholars have conducted studies at various scales and perspectives, focusing on aspects like carbon emission intensities [23], spatiotemporal variations [24], evolutionary patterns [25], driving factors [26], and optimization strategies [27]. The spatiotemporal effects, driving mechanisms, and optimization methods of land-use carbon emissions are current hotspots in research [28][29][30]. In the field of spatiotemporal effect research, scholars mainly focus on the changes in land-use types, the dynamic balance between carbon sources and carbon sinks, regional differences, and evolutionary characteristics [31], with investigative scopes encompassing multiple scales ranging from global [32] to national [33], provincial [34], city clusters [35], and urban areas [36].…”