Urban Land Carbon Emission and Carbon Emission Intensity Prediction Based on Patch-Generating Land Use Simulation Model and Grid with Multiple Scenarios in Tianjin

Abstract

With regard to the aims of achieving the “Dual Carbon” goal and addressing the significant greenhouse gas emissions caused by urban expansion, there has been a growing emphasis on spatial research and the prediction of urban carbon emissions. This article examines land use data from 2000 to 2020 and combines Grid and the PLUS model to predict carbon emissions in 2030 through a multi-scenario simulation. The research findings indicate the following: (1) Between 2000 and 2020, construction land increased by 95.83%, with carbon emissions also increasing. (2) By 2030, for the NDS (natural development scenario), carbon emissions are expected to peak at 6012.87 × 104 t. Regarding the ratio obtained through the EDS (economic development scenario), construction land is projected to grow to 3990.72 km2, with expected emissions of 6863.29 × 104 t. For the LCS (low-carbon scenario), the “carbon peak” is expected to be reached before 2030. (3) The intensity of carbon emissions decreases as the city size increases. (4) The shift of the center of carbon emission intensity and the center of construction land all indicate movement towards the southeast. Studying the trends of regional land use change and the patterns of land use carbon emissions is beneficial for optimizing the land use structure, thereby enabling us to achieve low-carbon emission reductions and sustainable urban development.

Contributing to Carbon Neutrality Targets: A Scenario Simulation and Pattern Optimization of Land Use in Shandong Province Based on the PLUS Model

“…Direct carbon emissions can be directly calculated using carbon emission coefficients. The calculation formula is as follows [17]:…”

“…Zhao et al [16] conducted calculations on the spatiotemporal distribution of carbon emissions in the Shandong Peninsula urban agglomeration based on LUCC data and characterized the spatial autocorrelation of LUCE at the urban level using Moran's I index. Li et al [17] utilized a carbon emission model to analyze the intensity zoning of LUCE in Tianjin City, uncovering a positive correlation between the expansion of construction land and the variation in LUCE intensity. Zhang et al [18] conducted carbon emission accounting at the township level to explore the spatial patterns of carbon emissions at a micro-scale level, aiming to assist cities in implementing refined governance.…”