Net primary productivity‐based factors of China's carbon intensity: A regional perspective

Abstract

Reducing carbon intensity (CI) is one of the core steps of climate change mitigation. This study emphasized the roles of ecological services and regional heterogeneity in determining CI. We considered heterogeneities based on geography and income and explored the roles of net primary productivity (NPP)-based carbon footprint and CI in the changes in China's CI over 2001−2015 using an extended production-theoretical decomposition analysis and matching the socioeconomic data sets with NPP data from a remote sensing satellite. We found that group technological change, reflecting the effect of shrinkage or expansion of the group best practice frontier, and the potential NPP-based carbon footprint, reflecting the impact of energy-related CO 2 emissions on ecological carbon absorption, are the most significant factors accounting for the increase and decrease in CI, respectively, while NPP-based CI generally accounts for the decline in CI. We further showed that the technology gap change exhibited by an invert U-shaped curve contributed to the increase in CI under geography-based heterogeneity. We advise that China's policies should be more focused on ecological factors and regional heterogeneity in regions with abundant NPP (e.g., Yunnan and Sichuan) to further reduce CI. 1728 | CHEN Et al. 1 | INTRODUCTION Global warming is one of the most challenging problems of the 21st century (Akorede, Hizam, Ab Kadir, Aris, & Buba, 2012). It is caused by greenhouse gases, with CO 2 emissions contributing up to 77% of the total anthropogenic emissions (Intergovernmental Panel on Climate Change, 2007). Tackling climate change requires responses from the economic system because of the policy implications on climate change mitigation (Mendelsohn, Nordhaus, & Shaw, 1994; Nordhaus, 1982). Carbon intensity (CI), or carbon emissions per unit of Gross Domestic Product (GDP), is an indicator for emission mitigation policy targets. A decline in CI indicates better low carbon development in economic terms. Compared to aggregate and per capita carbon emissions, CI reflects both emissions and economic performance (Dong et al., 2018). Therefore, CI is widely used as an indicator of emission mitigation by governments and researchers. For example, India plans to reduce its CI by 20%-25% between 2005 and 2020 (Stern & Jotzo, 2010), while Malaysia aims to reduce its CI by 40% in 2020 based on its 2005 levels (Mustapa & Bekhet, 2016). This is also particularly true for other developing countries, including China. China is the world's largest developing country. It has the world's largest population and is the second largest economy. The country has experienced high economic growth since the opening up reforms were instituted, with average annual growth exceeding 9% (Chen, Xu, Li, & Song, 2018). This rapid economic growth consumed a large amount of energy and resulted in massive CO 2 emissions. To address global warming and achieve low carbon development, the Chinese government has set a series of top-down policies. For example, China set targets to ...

Mapping and Influencing the Mechanism of CO2 Emissions from Building Operations Integrated Multi-Source Remote Sensing Data

“…Both of them are important parameters for carbon cycle since the consideration of the production of biomass or carbon in ecosystem [74][75][76] and existing studies have explored the performances of vegetation indices in estimating GPP and NPP [77][78][79]. In addition, previous studies also combined the NPP and economic factors to calculate carbon footprint pressure and analyze the driving factors of carbon intensity [80][81][82]. From the direct relationship with vegetation cover, GPP and NPP are alternative to be used to analyze the influencing factors of BCEs.…”

Carbon peak and its mitigation implications for China in the post-pandemic era

“…Similar to Ramaswami et al 51 and Tong et al 52 , we also divided the cities into three city groups by economic structure: the highly industrial in which the secondary industrial GDP% was higher than the national average plus one standard deviation, highly commercial where the tertiary industrial GDP percentage was higher than the national average plus one standard deviation, and mixed-economy cities that did not fall in the abovementioned two types. We also classified the provinces into three regions, i.e., the eastern, central and western regions according to Chen et al 53 .…”

Air Pollution and Migration

“…Such the house price of the focal city (price), logarithm value of the total population for each city in 2014; the population density (popd), proxied by the share of 10,000 persons in city area (km 2 ), we control for population density because it may affect air pollution (Chen et al, 2020a); public services, the share of 10,000 books and doctors in the total population (10,000 persons); removal rate of industrial wastes including solid waste (rsw) and waste water (rww). We could not include other factors, such as technological changes (Chen et al, 2020b;Chen et al, 2020c), because of this factor may exert more impacts on pollution (or pollution intensity) than labor mobility. Summary statistics are reported in Table 1.…”