Abstract
The rapid rate of urbanization is causing increasing annual urban energy usage, drastic energy shortages, and pollution. Building operational energy consumption carbon emissions (BECCE) account for a substantial proportion of greenhouse gas emissions, crucially influencing global warming and the sustainability of urban socioeconomic development. As a foundation of building energy conservation, determination of refined statistics of BECCE is attracting increasing attention. However, reliable and accurate representation of BECCE remains lacking. This study proposed an innovative downscaling method to generate a gridded BECCE intensity benchmark dataset with 1 km2 spatial resolution. First, we calculated BECCE at the provincial level by energy balance table application. Second, on the basis of building climate demarcation, partial least squares regression models were used to establish the BECCE behavior equations for three climate regions. Third, Cubist regression models were built, retrieving down scale at the prefecture level to 1 km2 BECCE, which well-captured the complex relationships between BECCE and multisource covariates (i.e., gross domestic product, population, ground surface temperature, heating degree days, and cooling degree days). The downscaled product was verified using anthropogenic heat flux mapping at the same resolution. In comparison with other published pixel-based datasets of building energy usage, the gridded BECCE intensity map produced in this study showed good agreement and high spatial heterogeneity. This new BECCE intensity dataset could serve as a fundamental database for studies on building energy conservation and forecast carbon emissions, and could support decision makers in developing strategies for realizing the CO2 emission peak and carbon neutralization.
Highlights
With rapid development of urbanization globally, greenhouse gases associated with the urban metabolism continue to be emitted in huge quantities, substantially influencing global warming and sustainable urban socioeconomic development [1,2,3]
We found that the controlling effect of the HDD had the greatest impact on Building operational energy consumption carbon emissions (BECCE) in region I, with a coefficient more than 2 and 8 microclimate, represented by ground surface temperature (GST), made the smallest contribution to BECCE in all three times higher than that of Gross domestic product (GDP) and POP, respectively
propose a new machine learning-based squares (PLS) was applied for constructing BECCE driving mechanism behavior equations because it could effectively offset the collinear contribution among driving factors
Summary
With rapid development of urbanization globally, greenhouse gases associated with the urban metabolism continue to be emitted in huge quantities, substantially influencing global warming and sustainable urban socioeconomic development [1,2,3]. It is predicted that the current rapid rate of development will continue to drive energy consumption and carbon emissions even higher in the future, increasing even greater urgency to the need to address climate change [5]. In this context, some countries’ national greenhouse gas emission reduction targets have been proposed. For China, the targets aim to reach the CO2 emission peak by 2030 and achieve carbon neutralization by 2060, with the ultimate objective of achieving net-zero CO2 emissions. 4.0/).
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