Table 1.
Literature on the driving factors of carbon emission from power industry
| Authors | Time period | Country/region | Indicator | Methods | Driving factors |
|---|---|---|---|---|---|
| An et al. (2022) | 2009–2016 | China | (CRP) carbon reduction potential) | LMDI (logarithmic mean Divisia index) | Coal-fired power generation efficiency, coal-fired power output rate, labor productivity, industry scale effect |
| Zhang et al. (2022) | 2005–2019 | Beijing,China | CO2 | LMDI | Emission factor effect, energy structure effect, conversion efficiency effect, power structure effect, power intensity effect, economic scale effect, population scale effect |
| Wang et al. (2021) | 1997–2017 | China | CO2 | Two-stage LMDI | Emission coefficient effect, TPGE effect, fossil fuel mix effect, nuclear effect, renewable effect, and total electricity generation effect |
| Luo et al. (2020) | 2007–2015 | China | CO2 | IO-SDA (input output structural decomposition analysis) model | Energy efficiency, production structure, consumption structure, and consumption volume |
| Mai et al. (2020) | 1998–2017 |
Northwest China |
CO2 | LMDI | Carbon intensity, energy mixes, generating efficiency, electrification, economy, and population |
| Wei et al. (2020) | 2007–2012 | Shanghai, China | CO2 |
LMDI SDA |
Fuel structure, energy efficiency, power structure, and power generation volume (LMDI); electricity consumption volume, electricity transmission structure, electricity transmission scale (SDA); carbon emissions intensity of electricity, electricity efficiency, production technology, consumption structure, consumption volume, carbon intensity of electricity, per capita electricity consumption and the population of Shanghai (SDA) |
| Wang et al. (2019) | 1991–2015 | 30 provinces, China | CO2 | Panel quantile regression model | The share of nonfossil fuel power generation, GDP per capita, the capital stock of electricity sector, the average utilization hours of power generation equipment, the ratio of regional import electricity to total electricity consumption, auxiliary power consumption rate, the logarithm of substation capacity of extra high voltage, the ratio of electricity consumption of high energy-consuming industries to social electricity consumption |
| Ma et al. (2019) | 2007–2015 | China | CO2 | SDA | Energy structure, technical factor, final use structure, and final use level |
| Liao et al. (2019) | 2005–2015 | 30 provinces, China | CO2 | LMDI | Energy structure effect, energy efficiency effect, electricity structure effect, electricity trade effect, electricity consumption scale effect |
| Peng and Tao (2018) | 1980–2014 | China | CO2 intensity | LMDI | Technological innovation, structural adjustment |
| Liu et al. (2017) | 2000–2014 | China | Aggregate carbon intensity | LMDI | Thermal efficiency effect, clean power penetration effect, the fossil fuel mix effect, and the regional shift effect |
| Wang et al. (2017) | 1995–2012 | Shandong, China | CO2 | LMDI | Electricity power production, power production structure, energy conversion efficiency, energy mix, emission factor |
| Zhang et al. (2017) | 1995–2014 | Beijing-Tianjin-Hebei region, China | CO2 | Hierarchical LMDI | Fuel mix, the coal consumption rate, power generation structure, the ratio of power generation to consumption, transmission, and distribution losses, production sectors’ electricity intensity, industrial structure, household electricity intensity, economic scale, and population size |
| Meng et al. (2017) | 2001–2013 | China | CO2 | Logarithmic linear equation | Total electricity consumption, nonfossil energy share of electricity generation, thermal power generation efficiency |