Personal GHG emissions accounting and the driving forces decomposition in the past 10 years

Personal greenhouse gas (PGHG) emissions were crucial for achieving carbon peak and neutrality targets. The accounting methodology and driving forces identification of PGHG emissions were helpful for the quantification and the reduction of the PGHG emissions. In this study, the methodology of PGHG emissions was developed from resource obtaining to waste disposal, and the variations of Shanghainese PGHG emissions from 2010 to 2020 were evaluated, with the driving forces analysis based on Logarithmic Mean Divisia Index (LMDI) model. It showed that the emissions decreased from 3796.05 (2010) to 3046.87 kg carbon dioxides (CO2) (2014) and then increased to 3411.35 kg CO2 (2018). The emissions from consumptions accounted for around 62.1% of the total emissions, and that from waste disposal were around 3.1%, which were neglected in most previous studies. The PGHG emissions decreased by around 0.53 kg CO2 (2019) and 405.86 kg CO2 (2020) compared to 2018 and 2019, respectively, which were mainly affected by the waste forced source separation policy and the COVID-19 pandemic. The income level and consumption GHG intensity were two key factors influencing the contractively of GHG emissions from consumption, with the contributing rate of 169.3% and − 188.1%, respectively. Energy consumption was the main factor contributing to the growth of the direct GHG emissions (296.4%), and the energy GHG emission factor was the main factor in suppressing it (− 92.2%). Green consumption, low carbon lifestyles, green levy programs, and energy structure optimization were suggested to reduce the PGHG emissions. © 2023, The Author(s).

Future outlook and influencing factors analysis of natural gas consumption in Bangladesh: An economic and policy perspectives

The development of mechanization and technology has triggered the rising energy consumption in various sectors. The study objective is to investigate the relationship between gas consumption share, energy intensity, economic structure share, per capita gross domestic value, and population at the sectorial level from 1994 to 2020. This study analyzes the factors in three ways: first, the study applies decomposition analysis between key factors. Second, an appropriate decoupling method is applied to investigate the impacting factors to check the driving factors affecting the decoupling using the logarithmic mean Divisia index method. Third, based on aggregated and sectorial variations, the ratio decomposition of each sector is estimated. The results show that the economic activity effect is the main driving factor in raising gas consumption, which increased by 15.11 crore Tk./million during 1994–2020. The energy intensity trend declined by 14.87 Mtoe/crore Tk., which shows an imperative role in natural gas consumption efficiency for production and in economy. The economic structure share provided a record decline by 52.79 crores Tk. during the COVID-19 situation while a significant increase in gas consumption was found until 2013, which could be significant under decomposition changes. Three decoupling states such as expansive negative decoupling, expansive coupling and weak decoupling were found in which the decoupling relationship was gradually a dominated by a weak decoupling. The sectorial index provides a maximum of weak decoupling in which energy-saving technology gap change was found in power, captive power, industry, tea estate, compressed natural gas, and fertilizer sectors. Finally, the scenarios analyzed that natural gas consumption estimates maximum gross domestic product and conserves more energy in the future. Briefly, the decoupling process and its leading factors were obvious among nine major sectors.

Analysis of regional differences and decomposition of carbon emissions in China based on generalized divisia index method

The achievement of China's carbon dioxide (CO2) emission reduction target is of great significance in the face of global climate change. Accurate identification of key factors that affect CO2 emissions can provide theoretical support to policymakers when designing related policies. Compared to the traditional method, the generalized Divisia index method (GDIM) can capture the influence of multiple scale factors on carbon emissions, providing new tools for studying the decomposition of carbon emissions. The article proposed a GDIM-based decomposition method to analyze the drivers that influence CO2 emissions in China from 2000 to 2017. The results indicate that investment activity is the primary element in promoting China's carbon emissions, followed by energy use and economic activities. On the contrary, investment carbon intensity is the vital inhibitory factor, followed by GDP carbon intensity. Specifically, the positive driving force of investment and energy use is gradually weakening, while the contribution of economic activities is continuously strengthening. The effectiveness of carbon emission reduction in the Northeast, East, and Southwest is actively promoting China's carbon emission reduction, while the effectiveness of CO2 emission reduction in the Northwest is not performing well. The findings provide support and reference for carbon emission control in China. (C) 2022 Elsevier Ltd. All rights reserved.

Decomposition of residential electricity-related CO2 emissions in China, a spatial-temporal study.

The COVID-19 pandemic brings a surge in household electricity consumption, thereby enabling extensive research interest on residential carbon emissions as one of the hot topics in carbon reduction. However, research on spatial-temporal driving forces for the increase of residential CO2 emissions between regions still remains unknown in terms of emissions mitigation in post-pandemic era. Therefore, we studied the residential CO2 emissions from the electricity consumption of China during the period 1997-2019. Afterward, the regional specified production emission factors, combining with electricity use pattern, living standard and household size, were modelled to reveal the spatial-temporal driving forces at national and provincial scales. We observed that the national residential electricity-related CO2 increased from 1997 to 2013, before fluctuating to a peak in 2019. Guangdong, Shandong and Jiangsu, from East China were the top emitters with 27% of the national scale. The decomposition results showed that the income improvement was the primary driving force behind the emission increase in most provinces, while the household size and production emission effects were the main negative effects. For the spatial decomposition, differences in the total households between regions further widen the gaps of total emissions. At the provincial scale of temporal decomposition, eastern developed regions exhibited the most significant decrease in production emissions. In contrast, electricity intensity effect showed negative emission influences in the east and central regions, and positive in north-eastern and western China. The research identified the different incremental patterns of residential electricity-related CO2 emissions in various Chinese provinces, thereby providing scientific ways to save energy and reduce emissions.

CO2 Reduction Potential from Efficiency Improvements in China’s Coal-Fired Thermal Power Generation: A Combined Approach of Metafrontier DEA and LMDI

Among the G20 countries, China is the only country to experience an increase in electricity generation from coal-fired thermal power plants from 2019 to 2020. This study aims to develop an analytical framework combining metafrontier data envelopment analysis with the logarithmic mean Divisia index for a detailed decomposition analysis of ‘mass-based’ energy-related CO2 reduction potential through efficiency improvements in coal-fired thermal power plants in China. The results show that inefficiency in power generation can be largely attributed to differences in the location of power plants and the production scale. Moreover, the impact of regional heterogeneity on the changes in power generation efficiency is more notable for the small–medium power plants in the northeast region than the large power plants in the western region in China. However, when focusing on the mass-based CO2 reduction potential associated with the regional heterogeneity, its positive effects in the western region for the large power plants are 6.2 times larger than that in the northeast region for the small–medium power plants. These results imply that an analysis that focuses only on the efficiency score would ignore the production scale of coal-fired thermal power plants and thus would fail to properly evaluate the environmental impacts associated with efficiency changes.

Drivers of CO2 Emissions: A Debt Perspective.

CO2 emissions and debt accumulation are twin threats to sustainable development. To fill the gap that few studies can untangle the reasons behind CO2 emissions from the debt perspective, we illustrate debt can cause CO2 emissions through various channels. We then examined how debt-based drivers impact emission trajectories. We use the logarithmic mean Divisia index (LMDI) method to decompose the emission changes into five factors. We make decomposition analyses between different country groups to identify their respective characteristics. Further, to investigate the potential financial crisis impacts, we consider the full period 2001-2019 and two sub-periods (pre- and post-2008). The results show that the gross domestic product (GDP) is always the biggest contributor to emissions, whose effect on advanced economies saw a bigger decrease after 2008 than that on emerging economies. Debt-GDP is second only to GDP in contributing to emissions. It has a similar impact on emissions before and after 2008 for advanced economies, while it rockets after 2008 for emerging economies. Private debt financing of fossil fuels is the prominent inhibitor for both economies, especially for emerging economies. It has a stronger mitigation impact after 2008 than before for emerging economies, while has the opposite change for advanced economies. Debt structure and fossil CO2 intensity have relatively smaller effects on emissions. The crisis is an opportunity to promote low-carbon development. Since the COVID-19 pandemic is analogous to the 2008 crisis in terms of debt level and emission change, we provide recommendations for emission mitigation in the post-pandemic context.