Spatially explicit carbon emissions from land use change: Dynamics and scenario simulation in the Beijing-Tianjin-Hebei urban agglomeration

Land use change is a critical enabler for climatic change and consequently becomes a key source of carbon emissions. It is pivotal to track changes in carbon emissions from diverse land uses and model their future patterns in a geographically explicit manner which could capture spatial configuration and temporal dynamics from fine-resolution analyses, particularly in urban agglomerations with intense human activities. Yet, spatially explicit land-use carbon emissions have been poorly investigated; especially, no existing research to our knowledge quantifies the carbon emissions from different construction land types and tracks their changes along the urban-rural gradients. To fill in this gap, we examined carbon emissions associated with land use change during 2000–2020 in the Beijing-Tianjin-Hebei (abbreviated as BTH) region, China, and then simulated future carbon emissions by adopting the PLUS model to project land use patterns in 2035 under three scenarios, including business as usual (BAU), cropland protection and grain security (CPGS), and low-carbon ecological security (LCES) at a 1 km resolution. Results showed that regional carbon emissions rose first and then dropped with total increase of 11,047.29 × 104 t during 2000–2020, approximate 10 times as the increase in carbon absorption (1106.89 ×104 t), indicating a big challenge toward carbon neutrality. The expansion of industrial and mining land and urban construction land contributed the most to emissions. Besides, both intensities of carbon emission and absorption presented spatial differentiation across urban-rural gradients and as urbanization accelerated, both urban and urban-rural fringe areas are considered the priority regions for CO2 reduction efforts. By 2035, the largest carbon emissions will occur in the BAU scenario, followed by the CPGS and LCES scenarios which will witness the positive change exceeding the negative change. These findings offer insights for optimizing territorial spatial pattern locally and provide spatially explicit information for implementing regional low-carbon policies around urban agglomerations worldwide.