Criteria for assessing carbon emissions peaks at provincial level in China

Abstract

China has pledged to peak carbon emissions by 2030 and neutralize emissions by 2060. There is an urgent need to develop a comprehensive and reliable methodology to judge whether a region has reached its carbon emissions peak (CEP), as well as to schedule and prioritize mitigation activities for different regions. In this study, we developed an approach for identifying the CEP status of 30 provincial areas in China, considering both the carbon emissions trends and the main socioeconomic factors that influence these trends. According to the results of the Mann-Kendall (MK) tests, changes in carbon emissions for the 30 provincial areas can be grouped into four clusters: those with significant reductions, marginal reductions, marginal increases, and significant increases. Then, total energy consumption (TEC), the proportion of coal consumption (PCC), the proportion of the urban population (PUP), the proportion of secondary industry (PASP), and per capita GDP (PGDP) were further identified as the main factors influencing carbon emissions, by applying Redundancy analysis (RDA) and Monte Carlo permutation tests. To balance efficacy with fairness, we assigned scores from 1 to 4 to trends in carbon emissions, and the Group Analysis results of the main influencing factors above except for TEC; for TEC, main basis is the relevant assessment results. And finally, according to the actual condition of total scores, provincial areas were assigned to the first, second, third and fourth stage of progress toward CEP, using the method of Natural Breaks (Jenks). Based on the method, differentiated plans should be adopted from the perspective of fair development and emissions reduction efficiency, in accordance with the basic principles of Doing the Best within Capacity and Common but Differentiated Responsibilities. This classification method can also be adopted by other developing countries which have not yet achieved CEP.

Keywords

• Carbon emission peak (CEP)
• Influencing factors
• Determination methods
• Stage classification
• Provincial level