Identifying drivers of county-level industrial carbon intensity by a generic machine learning framework

Abstract

Carbon intensity has been recognized as a measure of the relative change between carbon emissions and economic development in numerous policy documents at multiple levels. Counties as the basic governmental units for policy formulation and implementation remain largely unexplored in climate governance. This study makes an effort to fill in this gap by examining the dynamics and drivers of industrial carbon intensity (ICI) at the county level. Given the data limitations at both county and industrial levels, we develop a data-driven method that evaluates six commonly used machine learning algorithms to study county-level ICI accounted by statistical data. We find that energy consumption and labor scale are two consistent and significant drivers of ICI across all results, especially, energy consumption acts as the crucial driver. Accordingly, we divide the overall industrial sector into small and large sub-sectors to provide differentiated policy implications. Small sub-sectors with low energy and labor inputs prioritize production-driven ICI reduction strategies, because increasing energy consumption by introducing automated machinery leads to rapid economic growth and thereby negatively impacts ICI. For large sub-sectors with high energy and labor inputs, efficiency promotion through technological innovation is required for ICI reduction. As an example of scenario analysis, we show that an integrated intensity reduction scenario by reducing energy consumption and short-term increasing labor scale and industrial scale will make it possible for Manufacture of Furniture sub-sector in the studied county to achieve an ICI reduction of 68.7% by 2060 compared to the 2015 level. Overall, this study provides an integrated and generic framework for predicting ICI at the county level to overcome the context of data scarcity, laying a basis for making differentiated and applicable policies for ICI reduction at the sub-city level.