Factor Decomposition Analysis Research Articles

Factor decomposition analysis of urban transport CO2 emissions in Chinese mega cities: case study of Beijing, Shanghai, Guangzhou and Shenzhen

Factor decomposition analysis of urban transport CO2 emissions in Chinese mega cities: case study of Beijing, Shanghai, Guangzhou and Shenzhen

A decomposition analysis of sociodemographic factors and non-cigarette tobacco use as contributors to the change in smoking rates in South Africa between 2017/18 and 2021.

Trends data up to 2018 suggest that tobacco smoking was increasing in South Africa (SA), but only limited information is available on the impact of the COVID-19 pandemic on the current pattern of use of tobacco and nicotine products (TNPs). To assess trends in sociodemographic patterns in the use of TNPs and the extent to which sociodemographic factors and non-cigarette tobacco use may explain possible changes in rates of TNP use between 2017/18 and 2021. Data from three waves (2017, 2018 and 2021) of the South African Social Attitudes Survey (total N=8 140) were analysed. The sociodemographic correlates of current daily and non-daily use of factory-manufactured cigarettes (FMCs), roll-your-own (RYO) cigarettes, waterpipe/hubbly, electronic cigarettes (ECigs), snuff (a smokeless tobacco) and any combination of FMCs, RYO cigarettes and waterpipe/hubbly ('current smoking') in 2017/18 (pre-pandemic) were compared with those for 2021 (during the pandemic). Chi-square analyses and generalised linear models were used to compare the prevalence of TNP use between the two periods. A Blinda-Oaxaca decomposition analysis was also used to explore the roles of various sociodemographic factors, including any differences in the use of TNPs, that may explain any observed differences in smoking rates between 2017/18 and 2021. Current smoking increased significantly from 22.4% (95% confidence interval (CI) 20.4 - 24.5) in 2017/18 to 27.6% (95% CI 24.3 - 31.1) in 2021 (p=0.009). Smoking of FMCs did not change significantly overall between these two periods (20.1% v. 22.1%; p=0.240), except for a significant increase among those who self-identified as black African and a decrease among those who self-identified as coloured and those currently employed. However, the use of waterpipe/hubbly, ECigs, RYO cigarettes and snuff increased significantly. Women, individuals aged <35 years and black Africans had the largest increase in waterpipe/hubbly and ECig use. A decomposition analysis showed that increased waterpipe/hubbly and RYO use explained 52% and 15%, respectively, of the increase in smoking rates, while lower employment during 2021 compared with 2017/18 explained 15% of the increase in smoking rates during 2021. The use of all TNPs continued to increase in SA, particularly among women and unemployed people. These findings have policy and practice implications for addressing socioeconomic disparities in tobacco-related diseases.

Decoupling state of urban development and carbon emissions and its driving factors and predictions: A case study of Chengdu metropolitan area

Prioritizing the reduction of carbon emissions in key developing urban centers is a significant step toward achieving China's carbon peaking and neutrality goals. We examined the Chengdu metropolitan area as a case study. We proposed a coupled method for inversely estimating nighttime light and carbon emissions from energy consumption, enhancing the accuracy of fitting at the county scale and addressing the issue of missing energy consumption data. We integrated the Tapio decoupling model and the Logarithmic Mean Divisia Index (LMDI) model for comprehensive analysis, mitigating the limitations arising from the use of a singular model. Furthermore, we employed a multi-scenario analysis with an extended STIRPAT model to forecast the carbon emission trajectory of the Chengdu urban agglomeration and elucidate potential pathways for achieving carbon peaking and carbon neutrality goals. The findings indicated the following:(1)carbon emissions are predominantly concentrated in regions characterized by high levels of urban development. Forested land serves as the primary carbon sink; however, its capacity in the Chengdu metropolitan area fails to offset carbon emissions from energy consumption;(2)factor decomposition analysis revealed that economic development was the principal driver of carbon emissions, leading to an increase of 23.7964 million tons. Conversely, energy intensity emerged as the primary mitigation factor, reducing carbon emissions by 11.0677 million tons;(3)The Chengdu metropolitan area is projected to reach a carbon peak by 2030, with the multi-factor coordinated optimization (MFO) model representing the most favorable trajectory, with a projected peak of approximately 15.6998 million tons. However, to achieve carbon neutrality by 2060, there is still a surplus of approximately 10.4808 million tons.

Trend Changes and the Driving Forces of Environmental Indicators in Countries Worldwide: A Structural Change Analysis of Variations in CO2 Emissions and Eco-Efficiency

Many authors state that climate change is driven by increasing CO2 emissions worldwide. An understanding of the major driving forces affecting emissions over time in both developed and developing countries is important. Thus, in this study, structural break analysis is used to identify when the trends of environmental indicators—CO2 emissions and environmental efficiency (EE)—across countries worldwide change. Our findings revealed notable structural breaks occurring in countries in 1994, coinciding with ballooning fossil energy prices. Regarding CO2, 55 of 143 countries experienced a structural break. Furthermore, another wave of structural breaks emerged in 2014, corresponding to the implementation of CO2 emission reduction plans by certain nations. For CO2, 64 of 143 countries experienced a structural break. Upon detecting breakpoints and their trends, we utilized LMDI factor decomposition analysis to discern their driving factors, thereby elucidating the underlying dynamics. In Latin America and the Caribbean, most breakpoints were undesirable shifts, but recently, desirable shifts have increased in North America, Oceania, and Europe, which include many countries with high economic levels, improving energy-related factors. Sub-Saharan Africa can also be said to have undergone an undesirable shift regarding energy-related factors. This study clarifies the precise influences on the trend of CO2 emissions at the global level by identifying the point in time when there is a significant statistical, rather than a subjective, breakpoint.

Socioeconomic mental health disparities and income mobility in Australia: A longitudinal factor decomposition analysis

In recent years mental health disparity has been rising in Australia. The contributing factors to this rising disparity are not well understood. We investigated this by measuring and decomposing socioeconomic mental health inequality in Australia using the Household, Income and Labour Dynamics in Australia (HILDA) panel survey datasets between 2009 and 2017 employing the longitudinal factor decomposition method. In this research, we decompose the Income-Related Health Mobility (IRHM) and the Health-Related Income Mobility (HRIM) index. The IRHM index reflects the effect of the relationship between relative health changes at a constant income level, and the HRIM index represents changes in income ranking or income mobility with a fixed health outcome over time. Our findings suggest that poorer individuals cannot procure private health insurance, experience more long-term health conditions and have larger exposure to life shocks than richer individuals. Thus, long-term health conditions, private health insurance coverage, and number of life disruptions are the primary factors that negatively impact the IRHM index, thereby contributing to an increase in socioeconomic inequality in mental health in Australia. Moreover, the HRIM index exhibits a more pronounced adverse impact from labour force status compared to the relatively weaker positive effects observed in the IRHM index, resulting in a net negative effect from labour force status in this period. To address this, Australian governments should focus on implementing cost-effective intervention policies targeting disadvantaged populations to maximise health system resources and minimise mental health disparity by addressing the socioeconomic determinants of mental health.

A study on natural gas consumption forecasting in China using the LMDI-PSO-LSTM model: Factor decomposition and scenario analysis

Natural gas, as a clean and low-carbon energy resource, assumes a vital role in facilitating the transformation of the Chinese energy structure. Effectively forecasting its consumption holds great practical implications for the high-quality social development of China. Therefore, a hybrid model for forecasting natural gas consumption (NGC) in China is developed in this paper. Firstly, the Logarithmic Mean Divisia Index (LMDI) method is adopted to decompose the influencing factors of the NGC in China from 1994 to 2020, which revealed that the energy structure effect and the economic development effect have a positive promotion on NGC, while the energy intensity effect manifests a significant inhibition. Subsequently, based on the contribution rate of each factor, the particle swarm optimization (PSO) algorithm to optimize the long and short-term memory neural network (LSTM) model is constructed for NGC forecasting. Compared to other benchmark models, the PSO-LSTM model demonstrated a significant improvement in predictive accuracy, showcasing its valuable application in NGC prediction. Finally, the PSO-LSTM model is employed to analyze the scenario prediction of NGC development from 2021 to 2035. The forecast result indicated that the future NGC in China will show a yearly growing trend, which may lead to a serious imbalance between the supply and demand of natural gas, so the Chinese government should fully consider the energy security issue when formulating relevant policies.

Research on Carbon Emission Peak Prediction in China—Based on STIRPAT Model and Driving Factor Decomposition Analysis

Research on Carbon Emission Peak Prediction in China—Based on STIRPAT Model and Driving Factor Decomposition Analysis

Analysis of Growth Factors of the Four Asian Dragons Following the Asian Financial Crisis

Purpose - This study aims to identify implications from a factor decomposition analysis of the growth structure of four Asian dragons, Korea, Singapore, Hong Kong, and Taiwan, which have re-emerged as key regions in the fourth industrial revolution and the global value chain.&#x0D; Design/Methodology/Approach - With the Translog Production Function employed as the analysis model, the Stochastic Frontier Analysis model was used to derive total factor productivity (TFP), which was decomposed into four factors including returns to scale, technological changes, technical efficiency, and others.&#x0D; Findings - The results of the analysis conducted over different periods indicate that during periods of high growth, the economy’s scale effect largely contributed to total factor productivity. However, it became evident that the effect of technological changes had become increasingly important after going through the foreign exchange and financial crises.&#x0D; Research Implications - The new growth momentum in these four regions suggests a need to invest in human capital, research and development, and other areas to cope with the low-growth era. Although past high-growth was attributed to the returns to scale effect, the economic structures shifted in the direction of enhancing efficiency, and the effect of technological changes became a driving force while going through the crises. These results show a need to focus on technological advancement through active research and development investment to increase total factor productivity.

An urban metabolism framework purpose to assess the dynamics of linear alkylbenzenes (LABs) discharge flows and driving forces at the city level in Guangdong province, China

Linear alkylbenzenes (LABs) are a class of molecular markers derived from anthropogenic activities. A comprehensive understanding of the mechanism that determines their entry into anthroposphere, in terms of magnitude and pathway, is the prerequisite to establish effective mitigation measures. This study develops a methodology framework to analyze the source-sink interactions and driving factors of the direct and indirect LAB discharges from production and living activities in Guangdong Province, China from 2004 to 2017. Results indicated that the total LAB discharges of Guangdong into the environment were averaged at 2.9 kt yr−1, of which 61.9% originated from the Pearl River Delta (PRD) urban agglomeration. An average proportion of 76.0% was discharged into water bodies with the remaining released into land bodied. From 2014 to 2017, the LAB discharges increased by seven times, resulting from the steady increase of urban residential sources, while contribution from industrial sources continuously declined during the studied period. Meanwhile, the discharging hotspots expanded from Guangzhou city to other super-cities around it, including Shenzhen and Dongguan. The other cities exhibited a decreasing trend in discharges as a function of distance from these hotspot cities. The multisectoral sources of LABs differed considerably among cities, and the source contribution of each city changed significantly with progressive urbanization. The factor decomposition analysis indicated that LAB discharges in PRD cities primarily contributed by the pollutant concentration and reflected the treatment structure, while peripheral cities of the PRD mainly contributed by the per capita consumption and pollutant discharge per unit of GDP. Overall, our results provide a scientific database and supports for the regional co-remediation of anthropogenic pollution.

Firm heterogeneity in sources of changes in CO2 emissions from international container shipping

Container ships are the largest source of CO2 emissions in international shipping, and reducing their environmental impacts is crucial to reduce global CO2 emissions. However, the sources of changes in CO2 emissions are still unclear at the firm level, making it difficult to propose practical policies to stakeholders. In this study, we investigated firm heterogeneity in the sources of changes in CO2 emissions from international container shipping by a firm-level factor decomposition analysis. We estimated route-specific CO2 emissions during 2018–2020 for the top seven operating companies using comprehensive firm-level microdata. The results show that the combined annual CO2 emissions increased during 2018–2019, and transportation activity changes considerably contributed to the increase in CO2 emissions. The results during 2019–2020 show that changes in transportation networks had higher impacts in decreasing CO2 emissions than carbon intensity improvements; thus, establishing suitable regulations for network structures can effectively decarbonize container shipping.

Scenario analysis on carbon peaking pathways for China's aluminum casting industry

To support high-quality carbon peaking, a scenario analysis of carbon peaking pathways according to an industrial chain perspective considering both material and part production needs exploring. As a critical segment in the aluminum industrial chain, aluminum casting industry was focused on in this paper. A bottom-up scenario analysis approach was proposed for the carbon emission simulation of China's aluminum casting industry, including: (1) a carbon emission assessment model based on material flow analysis and life-cycle assessment, (2) key influence factor selection and variation scenario prediction, (3) static and dynamic scenario analysis, and (4) driving factor decomposition analysis. Key findings are as follows: (1) China's aluminum casting industry accounted for about 1.1% of the national carbon emissions in 2020; (2) the production stage accounted for around 15% of the total carbon emissions of China's aluminum casting industry in 2020; (3) carbon peaking probabilities before 2030 for the static and dynamic scenario analysis are around 60% and 35%, respectively; and (4) the carbon peaking result differences in whether considering the production stage are about 20% for the static simulation and average 40% for the dynamic simulation. These findings effectively support the formulation of a high-quality carbon peaking pathway.

Decomposition and Scenario Analysis of Factors Influencing Carbon Emissions: A Case Study of Jiangsu Province, China

It is crucial for China to take the characteristics and development stage of every province in the region into account in order to achieve the “dual carbon” development goal. Using data collected from 2000 to 2019, this study identifies the factors that influence carbon emissions using the logarithmic mean Divisia index (LMDI) method and establishes a revised stochastic impacts by regression on population, affluence, and technology (STIRPAT) model to investigate the effects of four key factors on carbon emissions in Jiangsu province: population size, economic output, energy intensity, and energy structure. The following conclusions were drawn: (1) energy intensity contributes to a slowed rate of carbon emission production in Jiangsu, whereas population size, economic output, and energy structure contribute to a pulling effect; (2) under different scenarios, Jiangsu’s carbon dioxide emissions peak at different times and reach different values; and (3) two low-carbon scenarios are more in line with the current development situation and future policy orientation of Jiangsu Province and are therefore better choices. Our policy recommendations are as follows: (1) the development of economic and social activities should be coordinated and greenhouse gas emissions should be reduced; (2) the province’s energy structure should be transformed and upgraded by taking advantage of the “dual carbon” development model; and (3) regionally-differentiated carbon emission reduction policies should be developed.

Decomposition Analysis of Regional Embodied Carbon Flow and Driving Factors—Taking Shanghai as an Example

Developing localized climate mitigation strategies requires understanding how national consumption drives local carbon dioxide (CO2) emissions from a sectoral perspective. Exploring the carbon footprint considering inter-provincial trade is vitally important; however, few studies have explored the production side of embodied carbon emissions and the drivers of embodied carbon. Here, we use the multi-regional input–output model to calculate the flow of embodied carbon between provincial departments on Shanghai’s production side in 2012, 2015, and 2017. We also establish a decomposition method for the embodied carbon index with small data demand. Our results show that from 2012 to 2017, the outflow increased and went to tertiary industries in neighboring and economically developed provinces. Among them, the activity effect drove the increase in carbon emissions, and the technique effect drove the reduction in embodied carbon. Surprisingly, we found that the low efficiency of the energy utilization of metal products and the unreasonable structure of other services increased embodied carbon emissions from 2012 to 2015. Sectors with high exogenous embodied carbon emissions are critical areas in which collaborative mitigation efforts between Shanghai and downstream provinces drive these emissions. Shanghai should avoid falling into the “low-carbon trap” of developing countries. It should continue to adjust its industrial structure and increase the use of low-carbon energy to achieve carbon reduction.

The drivers of private and public eco-innovations in six large countries

The purpose of this paper is to examine and compare the contributions to environmental innovation of the public sector and private sector, respectively, and to compare their determinants. We analyze the development over time of triadic patents, classified as environmental technological innovations, for six major patenting countries from 1990 to 2014. This is done using a factor decomposition analysis framework. The analysis is done at both country aggregate level, and for a set of specific technological topics: alternative energy production, energy conservation, agriculture and forestry, and waste management. Results indicate that there has been a shift at an aggregate level towards environmentally sustainable technologies. In the private sector, the shift can be attributed to changes in research priorities and an increased scale of R&D. In the public sector, increased patenting of environmentally sound technologies can be attributed to efficiency gains. The largest difference between the public and private sector is observed in R&D efficiency, where in the private sector, reductions in efficiency have contributed negatively to patent growth, whereas the opposite is true for the public sector. In both sectors, research focus has shifted towards energy-related technologies.

Development of gender inequality in self-rated health in the life-phase of raising children in Germany from 1994 to 2018 – A decomposition analysis of socioeconomic, psychosocial and family-related influencing factors

Extensive research has documented gender inequalities in self-rated health (SRH) to the disadvantage of women. However, little research has been done on how this gender gap has changed against the backdrop of social change. Using data from the German Socio-Economic Panel Study (GSOEP), this study addressed this issue and examined time-trends in SRH between 1994 and 2018 in women (n = 117,608) and men (n = 101.404) aged 30–49 years. In addition, we analyzed the role of socioeconomic, psychosocial and family-related factors as possible mediators influencing these trends. We performed logistic regression analyses for analyzing the time-trends and applied the Karlson-Holm-Breen (KHB) method for decomposing the time effect into direct and indirect parts via mediators.Over time, the chance of reporting good as well as poor SRH remained largely stable in both genders while the majority of socioeconomic and psychosocial factors pointed towards improvement. The decomposition analysis revealed a positive effect of most of these mediators on the time trend in SRH. After controlling for the mediators, the health trend became negative, leading to a decline in the proportion of good health over time by 5.4%-point and 4.3%-point in women and men, respectively. The same pattern was observed with respect to poor SRH. For both genders, the decline of economic worries and the rise in school education contributed most to the indirect time effect.Our findings indicate a positive development of key socioeconomic and psychosocial health determinants particularly for women, but no corresponding increase in SRH. Thus, gender inequality in SRH remained largely unchanged. However, our results suggest that SRH would have developed much worse if there had been no improvements in the health determinants considered. Further studies are needed to determine what other factors may have counteracted a positive health trend and stood in the way of narrowing the gender health gap.

Uncertainty in climate change impact studies for irrigated maize cropping systems in southern Spain

This study investigates the main drivers of uncertainties in simulated irrigated maize yield under historical conditions as well as scenarios of increased temperatures and altered irrigation water availability. Using APSIM, MONICA, and SIMPLACE crop models, we quantified the relative contributions of three irrigation water allocation strategies, three sowing dates, and three maize cultivars to the uncertainty in simulated yields. The water allocation strategies were derived from historical records of farmer’s allocation patterns in drip-irrigation scheme of the Genil-Cabra region, Spain (2014–2017). By considering combinations of allocation strategies, the adjusted R2 values (showing the degree of agreement between simulated and observed yields) increased by 29% compared to unrealistic assumptions of considering only near optimal or deficit irrigation scheduling. The factor decomposition analysis based on historic climate showed that irrigation strategies was the main driver of uncertainty in simulated yields (66%). However, under temperature increase scenarios, the contribution of crop model and cultivar choice to uncertainty in simulated yields were as important as irrigation strategy. This was partially due to different model structure in processes related to the temperature responses. Our study calls for including information on irrigation strategies conducted by farmers to reduce the uncertainty in simulated yields at field scale.

Trend and decomposition analysis of risk factors of childbirths with no one present in Nigeria, 1990–2018

ObjectivesTo assess the trend and decompose the determinants of delivery with no one present (NOP) at birth with an in-depth subnational analysis in Nigeria.DesignCross-sectional.SettingNigeria, with five waves of nationally representative...

China's carbon intensity factor decomposition and carbon emission decoupling analysis

China's carbon emissions have been ranking first in the world. This study filled in the gaps in research, decomposed carbon intensity from the perspective of time, space and industry. A decoupling effort model based on factor decomposition models was constructed to analyze the driving factors of carbon emissions and economic decoupling, which builded a foundation for achieving sustainable economic development. Using the Logarithmic Mean Divisia Index method (LMDI), the paper measured the carbon emission intensity of 29 provinces and cities in China from 1998 to 2019, and decomposed the decoupling effect between GDP and carbon emission on the basis of factor decomposition by tapio. The results showed that: (1) Carbon intensity declined first, then rise lightly, and finally declined steadily. For the primary industry and the tertiary industry, the carbon intensity declined steadily, while the carbon intensity increased accordingly to the overall carbon intensity. In terms of spatial evolution, the regional differences between different provinces decreased correspondingly. (2) The cumulative contribution rates of these three effects, i.e., technological progress, industrial structure and regional scale were 106.3299%, −15.1486% and 8.8188%, respectively. There were obvious differences of these cumulative contribution rates of carbon intensity among different provinces. (3) From the perspective of industrial, technological progress effect is the largest contribution for carbon intensity in the secondary industry. The Industrial structure effect mainly affects the primary and tertiary industries; and no significant difference in regional scale effect. (4) The decoupling effect gradually improved, and technological progress has played an absolute leading role in promoting the decoupling effect. Based on the research results, the key policy recommendation are put forward as follows: (1) Further improve the technological level and support clean technology enterprises. (2) Promote industrial upgrading in backward industrial provinces (3) Promote regional assistance and the introduction of high-quality foreign investment.

Factor decomposition and spatio-temporal difference analysis of China’s marine resource consumption intensity

Based on the connotation of the intensity of marine resources consumption, this paper measures the intensity of marine resources consumption in China’s coastal provinces from 1996 to 2015, reveals its spatio-temporal evolution characteristics, establishes a factor decomposition model by using the improved logarithmic mean Di exponential decomposition (LMDI), analyzes the factor contribution of changes in the intensity of marine resources consumption in China, and compares the differences. The results show that: (1) from 1996 to 2015, the overall consumption intensity of China’s marine resources increased first and then decreased steadily, and the consumption intensity of resources in the primary industry decreased steadily, ranking second. The resource consumption intensity of the tertiary industry is basically synchronized with the change trend of China’s marine resource consumption intensity; in the evolution of spatial pattern, the provinces with medium and high intensity of marine resource consumption gradually reduce, while the provinces with low intensity provinces gradually increase, and the regional differences gradually narrow. (2) The contribution rates of technological progress effect, industrial structure effect and regional scale effect to the decline of China’s marine resource consumption intensity are 78.224%, 18.334% and 3.442%, respectively; there are significant differences in factor decomposition effects among coastal provinces, among which Fujian is dominated by technological progress effect, Zhejiang, Shandong and Hainan are dominated by technological progress effect and regional scale effect, Tianjin, Hebei and Jiangsu are dominated by technological progress effect and industrial structure effect, while Liaoning, Shanghai, Guangdong and Guangxi are jointly driven by technological progress effect, industrial structure effect and regional scale effect to reduce the intensity of marine resource consumption. (3) From the perspective of the three marine industries, the effect of technological progress is the largest contribution within the secondary industry, accounting for 77.118% in total; the industrial structure effect has the largest contribution within the primary industry, accounting for 314.547% in total. There is no significant difference in regional scale effect among the three industries. Different provinces and regions should pay different attention to the implementation of technologies or measures for the intensive utilization of resources in the three marine industries.

Comparison of source apportionment approaches and analysis of non-linearity in a real case model application

Abstract. The response of particulate matter (PM) concentrations to emission reductions was analysed by assessing the results obtained with two different source apportionment approaches. The brute force (BF) method source impacts, computed at various emission reduction levels using two chemical transport models (CAMx and FARM), were compared with the contributions obtained with the tagged species (TS) approach (CAMx with the PSAT module). The study focused on the main sources of secondary inorganic aerosol precursors in the Po Valley (northern Italy): agriculture, road transport, industry and residential combustion. The interaction terms between different sources obtained from a factor decomposition analysis were used as indicators of non-linear PM10 concentration responses to individual source emission reductions. Moreover, such interaction terms were analysed in light of the free ammonia / total nitrate gas ratio to determine the relationships between the chemical regime and the non-linearity at selected sites. The impacts of the different sources were not proportional to the emission reductions, and such non-linearity was most relevant for 100 % emission reduction levels compared with smaller reduction levels (50 % and 20 %). Such differences between emission reduction levels were connected to the extent to which they modify the chemical regime in the base case. Non-linearity was mainly associated with agriculture and the interaction of this source with road transport and, to a lesser extent, with industry. Actually, the mass concentrations of PM10 allocated to agriculture by the TS and BF approaches were significantly different when a 100 % emission reduction was applied. However, in many situations the non-linearity in PM10 annual average source allocation was negligible, and the TS and BF approaches provided comparable results. PM mass concentrations attributed to the same sources by TS and BF were highly comparable in terms of spatial patterns and quantification of the source allocation for industry, transport and residential combustion. The conclusions obtained in this study for PM10 are also applicable to PM2.5.