China’s Carbon Footprint Based on Input-Output Table Series: 1992–2020

Abstract

Abstract:Reducing carbon emissions is a major concern for China's future. This paper explores the embodied carbon footprint of Chinese final demand from the point of view of industries. It uses the Matrix Transformation Technique (MTT) to update the input-output table series from 1992 to 2020 in China. Then, we measure the embodied carbon emissions for the period 1992-2020 from 29 industry producers to the final demand, covering urban and rural residential consumption, government consumption, fixed capital formation, and net exports. The results show that construction, other services, wholesale, retail trade, accommodation and catering, industrial machinery and equipment, transport, storage and postal services, and manufacture of foods and tobacco are the industries with the greatest carbon emissions from producers, while fixed capital formation and urban consumption are the largest emitters from the perspective of final demand. The embodied carbon emission multipliers for most of the industries are decreasing, while the total carbon emissions are increasing each year. The ratio of emissions from residential consumption in terms of total emissions is decreasing. Each industry has a different main final demand-driven influencing factor on emission and, for each type of final demand, there are different industries with higher emissions.

The Higher Carbon Intensity of Loans, the Higher Non-Performing Loan Ratio: The Case of China

“…Because the industry classification in the input-output table series published by Zheng et al [13] is different from that in China Statistical Yearbook, we provide a reconciliation in this paper. We firstly adjusted the industries in China Statistical Yearbook into 29 categories, and then merged the 42 industries from input-output tables into the 29 industries according to the same standard.…”

“…We also calculated carbon emissions by the net Calorific Basis and Carbon Oxidation Factor from Provincial Greenhouse Gas Inventories. It should be particularly noted that the sectors divided in the China Energy Statistical Yearbook are different from those in input-output table published by Zheng et al [13]. We finally obtained 29 industries by considering the adjustment in "National Industry Classification" (GB/T4754-2011).…”

“…Prior to this, we need to firstly estimate carbon emissions based on an input-output analysis. Zheng et al [13] constructed China's input-output table series from 1992 to 2020 based on the matrix transformation technique (MTT) method. The main idea of MTT method is to release the constraints in benchmark input-output tables, and then interpolate the other years' unconstrained tables, and finally back-transform the other years' unconstrained tables into input-output tables.…”

The Gigantism of Public Works in China in the Twenty-First Century

“…The industry emits nearly 900 kg of CO2 for every 1000 kg of cement produced [22], and studies made in China show that, for the entire iron and steel production process, the base-case CO2 emission intensity is nearly 2150 kg per tonne of crude steel [23]. In China, construction, the most polluting sector, is alone responsible for 28% of the country's CO2 emissions [24]. This study shows the trend of large works in Asia and more especially in China, but also reflects the real situation of the whole country in terms of constructions.…”

Ecological Pressure of Carbon Footprint in Passenger Transport: Spatio-Temporal Changes and Regional Disparities

“…For this class, a system dynamics method has been used to simulate scenarios of potential urban CO 2 emission mitigation, depending on the selection of commuter transport mode, based on historical data in the USA [12]. Carbon emissions of China's industrial sector over the period of 1992-2020 have also been modeled using input-output tables [13]. In the United States, a life cycle analysis was performed for the consumption and pollution of aircraft and intercity bus emissions [14] using three independent variables, i.e., the size of the population; gross domestic product (GDP); the number of small-, medium-, and large-sized registered vehicles.…”