Carbon Emission Inversion Model from Provincial to Municipal Scale Based on Nighttime Light Remote Sensing and Improved STIRPAT

Abstract

Carbon emissions and consequent climate change directly affect the sustainable development of ecological environment systems and human society, which is a pertinent issue of concern for all countries globally. The construction of a carbon emission inversion model has significant theoretical importance and practical significance for carbon emission accounting and control. Established carbon emission models usually adopt socio-economic parameters or energy statistics to calculate carbon emissions. However, high-precision estimates of carbon emissions in administrative regions lacking energy statistics are difficult. This problem is especially prominent in small-scale regions. Methods to accurately estimate carbon emissions in small-scale regions are needed. Based on nighttime light remote-sensing data and the STIRPAT (Stochastic Impacts by Regression on Population, Affluence, and Technology) model, combined with the environmental Kuznets curve, this paper proposes an ISTIRPAT (Improved Stochastic Impacts by Regression on Population, Affluence, and Technology) model. Through the improved STIRPAT model (ISTIRPAT) and panel data regression, provincial carbon emission inventory data were downscaled to the municipal level, and municipal scale carbon emission inventories were obtained. This study took the 17 cities and prefectures of Hubei Province, China, as an example to verify the accuracy of the model. Carbon emissions for 17 cities and prefectures from 2012 to 2018 calculated from the original STIRPAT model and the ISTIRPAT model were compared with real values. The results show that using the ISTIRPAT model to downscale the provincial carbon emission inventory to the municipal level, the inversion accuracy reached 0.9, which was higher than that of the original model. Overall, carbon emissions in Hubei Province showed an upward trend. Regarding the spatial distribution, the main carbon emission area was formed in the central part of Hubei Province as a ring-shaped mountain peak. The lowest carbon emissions in the central area expanded outward, increased, and gradually decreased to the edge of the province. The overall composition of carbon emissions in eastern Hubei was higher than those in western Hubei.

Optimization of Urban Road Green Belts under the Background of Carbon Peak Policy

“…This paper divides the variables into four elemental categories, namely environmental, economic, demographic, and technological, and considers urban as well as road characteristic indicators [27,41], as detailed in Table 1.…”

“…The STIRPAT model or improved STIRPAT model, which can consider many influence factors, is used to test the carbon emission peak and carbon neutrality [21][22][23][24][25][26]. Population, energy intensity, industrial structure, urbanization level, investment in fixed assets, urbanization, vehicle ownership, and gross domestic product (GDP) per capita, among other influencing factors, are often utilized in the STIRPAT model to predict carbon emissions [27,28]. Machine-learning methods have also been used to predict carbon emission, but they require large amounts of data, and because they are black boxes, continuously determining the nonlinear changing trend of the influence of independent variables on dependent variables is difficult [29].…”

Spatiotemporal characteristics and zonal analysis of carbon balance in county-level regions: case study of Jiangsu Province

“…Commonly used methods include carbon emission coefficient (Shan et al, 2016), material balance, and direct measurement (Liu et al, 2014). With the increasing availability of remote sensing data, the simulation and estimation of carbon emissions using nighttime light data have become one of the prevailing methods for estimating multi-scale carbon emissions (Shi et al, 2016;Sun et al, 2020;Wang et al, 2022). Scholars have widely adopted this approach to estimate carbon emissions in energy consumption.…”

Characteristics of Land-Use Carbon Emissions and Carbon Balance Zoning in the Economic Belt on the Northern Slope of Tianshan

“…Several statistical methods and analytical approaches were widely applied for the related studies on land-use carbon emissions (Table 1) [24,25]. Recently, remote sensing (RS) technology was proven to be a powerful tool for CEs estimation due to its transparency, multi-time qualities, and wide coverage [26,27]. However, to create an inventory of CO 2 and carbon stock changes over time, the combination of more software with remote sensing technology is necessary [28,29].…”