A process-level hierarchical structural decomposition analysis (SDA) of energy consumption in an integrated steel plant

A hierarchical structural decomposition analysis(SDA) model has been developed based on process-level input-output(I-O) tables to analyze the drivers of energy consumption changes in an integrated steel plant during 2011-2013. By combining the principle of hierarchical decomposition into D&L method, a hierarchical decomposition model for multilevel SDA is obtained. The developed hierarchical IO-SDA model would provide consistent results and need less computation effort compared with the traditional SDA model. The decomposition results of the steel plant suggest that the technology improvement and reduced steel final demand are two major reasons for declined total energy consumption. The technical improvements of blast furnaces, basic oxygen furnaces, the power plant and the by-products utilization level have contributed mostly in reducing energy consumption. A major retrofit of ancillary process units and solving fuel substitution problem in the sinter plant and blast furnace are important for further energy saving. Besides the empirical results, this work also discussed that why and how hierarchical SDA can be applied in a process-level decomposition analysis of aggregated indicators.

Structural Decomposition Analysis to Investigate the Changes in Energy Consumption in Pakistan

The world energy demand is increasing due to rapid growth in the global economy,industrialization,and urbanization.Pakistan is also confronted with increasing energy demand on one hand and is confronted with the challenge of energy demand-supply gap on the other hand.Since energy is the major driver for growth,it becomes important to investigate the trends of energy consumption in a country and the factors that are most affecting the changes in the use of energy.This particular study aims to investigate the use of energy by all the economic sectors of Pakistan during 2000–2012.The major contribution is the first time application of structural decomposition analysi for energy usage along with using Input-Output data for the period of 2002–2012.The results show the fluctuation of the energy intensity of the sectors throughout the study period.Also,the overall effect of energy intensity is negative on energy consumption and it shows a negative contribution value of-80.90%for the study period.Furthermore,the focus on more energy-intensive products like cement,automobiles,iron,steel products and the increasing final demand of the economy contributes to the growth of energy consumption in Pakistan during 2000–2012.

Decomposition analysis applied to energy and emissions:A literature review

Decomposition analysis has been widely used to assess the determinants of energy and CO_(2)emissions in academic research and policy studies.Both the methodology and application of decomposition analysis have been largely improved in the past decades.After more than 50 years’developments,decomposition studies have become increasingly sophisticated and diversified,and tend to converge internally and integrate with other analytical approaches externally.A good understanding of the literature and state of the art is critical to identify knowledge gaps and formulate future research agenda.To this end,this study presents a literature survey for decomposition analysis applied to energy and emission issues,with a focus on the period of 2016–2021.A review for three individual decomposition techniques is first conducted,followed by a synthesis of emerging trends and features for the decomposition analysis literature as a whole.The findings are expected to direct future research in decomposition analysis.

Research on the Influencing Factors of Changes in Industrial Carbon Emissions in China: Based on Simplified D&L Model

The simplified D＆L method with special properties required by ideal decomposition method was used to decompose the impact of carbon emission intensity,input-output technology,the final demand structure and the final demand level on changes in industrial carbon emissions in China during 1997-2012. The results showed that the final demand level which was the most important factor leading to the growth of carbon emissions performed a steadily and significantly positive and sustained effect. The carbon emissions intensity which was the only factor that led to the reduction in carbon emissions showed a negative effect. The input-output technology showed a positive effect. The final demand structure underwent a transition from a negative effect to a weak positive effect and finally to a positive effect. In order to achieve the goal of total carbon emission control,China should take some measures such as reducing direct carbon emission coefficient to strengthen the negative impact of carbon emission intensity. Meanwhile,China should implement some structural adjustment measures such as optimizing the final demand structure and reducing the proportion of industries with a great increase of influence coefficient to change the input-output technology and the final demand structure from the positive effect to the negative effect.

广东省煤炭消费的动态演变及其驱动机制

Guangdong Province,as one of China’s fast-developing regions,an important manufacturing base,and one of the national first round low-carbon pilots,still faces many challenges in controlling its total energy consumption.Coal dominates Guangdong’s energy consumption and remains the major source of CO_(2).Previous research on factors influencing energy consumption has lacked a systematic analysis both from supply side(factors related to scale,structure,and technologies)and demand side(investment,consumption,and trade).This paper develops the logarithmic mean Divisia index(LMDI)method that focuses on the supply side and the structural decomposition analysis(SDA)method that focuses on the demand side to systematically identify the key factors driving coal consumption in Guangdong.Results are as follows:(1)Supply side analysis indicates that economic growth has always been the most important factor driving coal consumption growth,while energy intensity is the most important constraining factor.Industrial structure and energy structure have different impacts on coal consumption control during different development phases.(2)Demand side analysis indicates that coal is consumed mainly for international exports,inter-provincial exports,fixed capital formation,and urban household.(3)Industries with the fastest coal consumption growth driven by final demand have experienced significant shifts.Increments in industrial sectors were mainly driven by inter-provincial exports and urban household consumption in recent years.(4)Research on energy consumption in subnational regions under China’s new development pattern of“dual circulation”should not only focus on exports in the context of economic globalization but also pay more attention to inter-provincial exports on the background of strengthened interregional connections.

Supply-side drivers of phosphorus emissions from phosphorus supply chains in China

Human activities interfere with natural Phosphorus(P)cycles by introducing increased levels of P emissions to air,land,and water.A supply-side analysis of P supply chains and associated P emissions can provide insights into underlying economic activities and transitions responsible for human-induced P emissions.Taking the China's Mainland as the case,this study constructs time-series physical input-output tables to describe P supply chains during 1949-2012.Subsequently,it identifies critical products and influencing factors of P supply chains enabling P emissions to the environment(including air,land,and water)from the supply perspective.The results show that phosphate rock,an important initial supplier of P from natural environment to China’s P supply chain,was responsible for 86%of P emissions in 2012.Moreover,food crops and livestock are important initial suppliers of P from soil to China’s P supply chain,through cultivation and pasturing,respectively.From 1949 to 2012,the change in primary input level was the largest driver of P emission increments,followed by changes in population,emission intensity,and primary input structure.On the contrary,changes in production structure reduced P emissions.These findings could support supply-side policy decisions on P emission control.

Air pollutant emissions induced by rural-to-urban migration during China's urbanization(2005-2015)

As the world's most populous country,China has witnessed rapid urbanization in recent decades,with population migration from rural to urban(RU)regions as the major driving force.Due to the large gap between rural and urban consumption and investment level,large-scale RU migration impacts air pollutant emissions and creates extra uncertainties for air quality improvement.Here,we integrated population migration assessment,an environmentally extended inputeoutput model and structural decomposition analysis to evaluate the NOx,SO2 and primary PM2.5 emissions induced by RU migration during China's urbanization from 2005 to 2015.The results show that RU migration increased air pollutant emissions,while the increases in NOx and SO2 emissions peaked in approximately 2010 at 2.4 Mt and 2.2 Mt,accounting for 9.2%and 8.7%of the national emissions,respectively.The primary PM2.5 emissions induced by RU migration also peaked in approximately 2012 at 0.3 Mt,accounting for 2.8%of the national emissions.The indirect emissions embodied in consumption and investment increased,while household direct emissions decreased.The widening gap between urban and rural investment and consumption exerted a major increasing effect on migration-induced emissions;in contrast,the falling emission intensity contributed the most to the decreasing effect benefitting from end-of-pipe control technology applications as well as improving energy efficiency.The peak of air pollutant emissions induced by RU migration indicates that although urbanization currently creates extra environmental pressure in China,it is possible to reconcile urbanization and air quality improvement in the future with updating urbanization and air pollution control policies.

Research on the Determinants of Government Investment Effect

Government investment plays an important role in promoting and guiding the economic and social development in China.Xinjiang is the core province of the Belt and Road and its economic growth is mainly driven by investment.This paper decomposes the determinants of government investment effect into economic structure change,technological change and investment structure change,using the non-competitive input-output table of Xinjiang province in 2007,2012 and 2015 and structural decomposition analyses.The results show that,the government investment effect in Xinjiang shows a slight decline trend.During the period,the change of economic structure and investment structure have the negative impact on government investment effect,while the change of technology has the positive impact on government investment effect.In addition,these impacts have strong sectoral heterogeneity.

Factors influencing polychlorinated dibenzo-p-dioxin and polychlorinated dibenzofuran (PCDD/F) emissions and control in major industrial sectors: Case evidence from Shandong Province, China

Analyzing determinants that influence polychlorinated dibenzo-p-dioxin and polychlorinated dibenzofuran（PCDD/F） emissions is helpful for decision-makers to find effective and efficient ways to mitigate PCDD/F emissions. The PCDD/F emissions and the contributions of the scale effect, structure effect and technology effect to emissions from eight main industrial sectors in2006, 2008 and 2010 in Shandong Province, were calculated in this article. Total PCDD/F emissions in Shandong increased by 52.8% in 2008（614.1 g I-TEQ） and 49.7% in 2010（601.8 g I-TEQ） based on 2006（401.9 g I-TEQ）. According to the decomposition method, the largest influencing factor on PCDD/F emission changes was the composition effect（contributed 43.4%in 2008 and 120.6% in 2010 based on 2006）, which was also an emission-increasing factor.In this case, the present industrial restructuring policy should be adjusted to control the proportion of production capacities with high emission factors, such as iron ore sintering and steelmaking and the secondary non-ferrous metal sector. The scale effect increased the emissions in 2008（contributed 21.9%） and decreased the emissions in 2010（contributed-28.0%）. However, as a source control measure, the excess capacity control policy indeed had a significant role in emission reduction. The main reason for the technology effect（contributed 34.7% in 2008 and 7.4% in 2010 based on 2006） having an emission-increasing role was the weakness in implementing policies for restricting industries with outdated facilities. Some specific suggestions were proposed on PCDD/F reduction for local administrators at the end.