Energy-related Carbon Emissions Research Articles

Decomposition and decoupling analysis of energy-related carbon emissions in Turkey.

This study focuses on CO2 emission trends and its decompositions as well as decoupling performance between CO2 emissions and economic growth of Turkish case for the period of 1990-2016. The drivers of CO2 emission changes are calculated by using an extended Kaya identity and the well-established logarithmic mean Divisia index (LMDI) method. Decomposition results indicate that economic growth and population effects are the main driving forces in increases in carbon emissions in Turkey throughout the whole period, while other technology-based driving factors' impacts have been rather minimal in reducing the emissions. Decoupling analysis results demonstrate that there is either no decoupling or weak decoupling in most of the years. Moreover, total decoupling effort index suggests that Turkey's performance has been worsened in recent years as we found no decoupling between CO2 emissions and economic growth over the period of 2013-2016. The overall findings suggest that Turkish economic growth is unsustainable both environmentally and economically. Based on these findings, some policy implications and recommendations are discussed for the possible emission reductions.

Hierarchical structure analysis of urban carbon metabolism: A case study of Beijing, China

Abstract An integral analysis of urban carbon metabolism can support efforts to build a more sustainable city: the spatial gradients provide insights into how to guide future land use and cover change, while ecological network hierarchy provide insights into the self-mutualistic interactions of the city’s carbon metabolic structure. In this study, we combined a spatially explicit network with carbon budget of energy consumption and land use and cover change in Beijing, China. From 1990 to 2010, energy related carbon emissions accounted for roughly 90% of the city’s total. The network’s integrated flux decreased at an average rate of 7.5%. The output flux primarily decreased when moving outward from the center through the periphery of southeastern plains and towards the northwestern mountains. In contrast, the input flux increased moving from the periphery to both the center and mountains. The ecological hierarchy was irregular based on both the driving and pull weights for the components of the model, with excessively small producer and decomposer layers and excessively large consumer layers. The changing structure of Beijing’s carbon metabolism over time indicated a tendency to a better organized city. Understanding the evolution of these hierarchies during urban development provides a more solid empirical basis for improving use of the urban space and reducing carbon emission.

Energy transition and technical energy regulations in the building sector

Energy demand from buildings accounts for about 31% of global final energy demand and 23% of global energy-related carbon emissions. Technical energy regulations or building energy codes - policies that set minimum requirements for energy in buildings – have proven effective and efficient in decarbonizing the building sector. However, despite their long history and success, policymakers increasingly recognise that TERs in their current design have reached a point of diminishing returns. This study evaluates five countries with innovative building energy codes – Denmark, France, England, Switzerland, and Sweden – through reviewing legal documents and conducting expert interviews with researchers, practitioners, and regulators. Our results highlight the implementation challenges of innovative building energy codes and we provide learnings in form of six design principles.

Analyzing Carbon Emissions Embodied in Construction Services: A Dynamic Hybrid Input–Output Model with Structural Decomposition Analysis

The energy embodied in construction services consumed by industrial sectors used to increase capacities has led to massive energy-related carbon emissions (ERCE). From the perspective of consumer responsibility, ERCE embodied in construction services is driven by technological changes and the increases in final demand of various sectors, including final consumption, fixed assets investment, and net export. However, little attention has been paid to decomposing sectoral responsibilities from this perspective. To fill this research gap, we propose a dynamic hybrid input–output model combined with structural decomposition analysis (DHI/O-SDA model). We introduce DHI/O modeling into the estimation of ERCE embodied in construction services from the perspective of consumer responsibility and introduce SDA into DHI/O models to improve the resolution of the estimate. Taking China as a case study, we verified the DHI/O-SDA model and present the bilateral relationships among sectoral responsibilities for ERCE embodied in construction services. A major finding is that the “Other Tertiary Industry” sector is most responsible for ERCE embodied in construction services and strongly influences other sectors. Therefore, controlling the final demand increase of the service industry will be the most effective policy to reduce the ERCE embodied in construction services.

Extended STIRPAT model-based driving factor analysis of energy-related CO2 emissions in Kazakhstan.

Extended stochastic impact by regression on population, affluence, and technology model incorporating ridge regression was used to analyze the driving mechanism of energy-related CO2 emissions in Kazakhstan during 1992-2014. The research period was divided into two stages based on GDP of Kazakhstan in 1991 (85.70 × 109 dollars), the first stage (1992-2002), GDP < 85.70 × 109 dollars, the stage of economic recovery; the second stage (2003-2014), GDP > 85.70 × 109 dollars, the stable economic development stage. The results demonstrated that (1) population scale and the technological improvement were the dominant contributors to promote the growth of the CO2 emissions during 1992-2014 in Kazakhstan. (2) Economic growth and industrialization played more positive effect on the increase of the CO2 emissions in the stable economic development stage (2003-2014) than those in the stage of economic recovery (1992-2002). The proportion of the tertiary industry, the trade openness, and foreign direct investment were transformed from negative factors into positive factors in the stable economic development stage (2003-2014). (3) Due to the over-urbanization of Kazakhstan before the independence, the level of urbanization continued to decline, urbanization was the first factor to curb CO2 emissions during 1992-2014. Finally, some policy recommendations are put forward to reduce energy-related carbon emissions.

Measuring whether municipal climate networks make a difference: the case of utility-scale solar PV investment in large global cities

ABSTRACTWith cities being responsible for up to 70% of energy-related carbon emissions, municipal governments worldwide are becoming increasingly aware of their responsibility to act. Many large cities have committed to mitigation by becoming member of a municipal climate network, such as the C40 or the Compact of Mayors. However, there is no consistent assessment of whether membership of such networks translates into measurable outcomes. To fill this gap, we propose the use of novel outcome variables, combining financial data with geospatial information. As a starting point, this paper compares utility-scale investment in photovoltaics (PV) within the administrative boundaries of large global cities, combining the Bloomberg New Energy Finance database with information from Google Maps. We analyse 512 global cities with a population of above 1 million, and consider the impact of 5 networks and 2 reporting platforms. The results suggest that membership of the C40 network has a positive effect on utility-scale solar PV investment, while no such evidence is found for any of the other networks or reporting platforms under study. Based on our findings, we recommend that municipal climate networks increase their efforts to trigger city regulation that is conducive to solar PV investment. More generally, measuring early indicators, such as low-carbon investment, can help municipal climate networks in their role as ‘commitment brokers’ for climate action on the ever-more important city level.Key policy insightsCities have considerable policy space to foster utility-scale solar PV investment within their administrative boundaries.While some large global cities exhibited significant growth in utility-scale solar PV, many others with good solar potential did not have a single project by the end of 2016.Outside of China (where city boundaries often include rural areas), Tokyo tops the list with utility-scale solar PV projects by far, followed by San Diego and Rome.Membership of the C40 network appears to make a positive difference to PV investment, unlike other networks or reporting platforms.Outcome measures like low-carbon investment can be used more generally to assess the climate action performance of cities.

Multiangle land use-linked carbon balance examination in Nanjing City, China

Land use change can greatly affect the carbon balance for both terrestrial ecosystems and anthropogenic carbon emissions. However, “physical-social” integrated carbon balance examinations and the link to multiangle land use change analysis are still lacking. Based on land use data, field observations, climate data, and socioeconomic data, this study performed a comprehensive analysis of land use change from the aspects of land use type, land cover and land use intensity and examined the effect on carbon storage, the net ecosystem productivity (NEP) and energy consumption-related carbon emissions. Finally, this study proposed multiangle land use strategies for low-carbon development. The results show that built-up land occupation of cropland was the main land use type change pattern in Nanjing City. The period of 2005–2010 presented the most obvious land use type change. Between 2000–2015, vegetation carbon storage loss reached 8.21×104 t, with woodland shrinkage contributing the most. The initial core city and the peripheral area show the most obvious land use type changes, with decreases in the Normalized Difference Vegetation Index (NDVI) and NEP. For all of Nanjing City, the NDVI followed a weak increasing trend from 2000 to 2015, and the NEP also exhibited a weak increasing trend for areas without built-up land and water. Spatially, the NDVI and NEP present similar changing trends for most grids. Land use intensities were continuously increasing, and energy-related carbon emissions increased from 4389.35×104 t in 2000 to 10145.09×104 t in 2015, with the industry sector contributing more than 80% of the total carbon emissions. In total, NEP accumulation can offset 2% of energy-related carbon emissions. Low-carbon land management should be strengthened from multi-land use aspects, including urban expansion control, ecological land protection, and land use efficiency.

Analysis of the Influence Mechanism of Energy-Related Carbon Emissions with a Novel Hybrid Support Vector Machine Algorithm in Hebei, China

Analysis of the Influence Mechanism of Energy-Related Carbon Emissions with a Novel Hybrid Support Vector Machine Algorithm in Hebei, China

Source control or end-of-pipe control: Mitigating air pollution at the regional level from the perspective of the Total Factor Productivity change decomposition

This paper investigates the key paths for mitigating air pollution following the paths of the source control and end-of-pipe control. We conduct performance analysis in terms of energy consumption and pollution emission from the regional perspective, and identify the appropriate path for each region of China. Our findings show that sulfur dioxide, primary energy consumption and energy-related carbon emission have become the main sources of inefficiency in the sense of the atmosphere environment pollution. Furthermore, we facilitate decomposition of the Luenberger productivity indicator (LPI) with regards to input/output variables and productivity change sources. The average annual productivity change for China is 1.36% over 2006–2013. LPI change associated with energy and pollutant variables shows the gradient which increases from southeast coast to west inland and decreases from north to south. This pattern implies the government needs to enhance the environmental regulation in the western regions. Moreover, the provinces are divided into five groups to identify the corresponding paths (source or end-of-pipe control). According to our identification, we suggest that local government should perform diversified policies suitably to strengthen source or end-of-pipe governance. This allows setting the targets for the government and further improving the effectiveness of policy measures within each region.

Impact assessment of supply-side and demand-side policies on energy consumption and CO2 emissions from urban passenger transportation: The case of Istanbul

The transportation sector accounts for about a quarter of global energy consumption and energy-related carbon emissions. To design and realize sustainable urban transportation, it is vital to understand and analyze interactions between a set of dynamic factors that shape transportation patterns, behaviors, and impacts. To this end, this study aims to develop a systems dynamics (SD) model for Istanbul, Turkey to simulate its urban motorized passenger transport system for analyzing numerous policies under different scenarios and assessing their potential effects in reducing energy consumption and CO2 emissions in the upcoming years. The constructed SD model includes four subsystems: population, household disposable income, transport, and energy and CO2 emissions. Based on historical data (2000–2015) and model validation processes, the energy consumption and the associated CO2 emissions from motorized passenger transport are forecasted for the following scenarios. The first one is business as usual scenario (BAU) which is designed to show how energy use and the associated CO2 emissions would evolve over time with the current development plans. The second and third scenarios constitute supply management measures (SMM) which consider different levels of improvements in the fuel economy of the vehicle fleet and reduced carbon emission intensity in electricity generation through increased share of renewable energy use. The fourth and fifth scenarios consider travel demand management (TDM) policies that include different levels of transport cost increase, and trip length reduction. Finally, the last two scenarios include integrated scenarios that are composed of the SMM and TDM options. In detail, compared to the BAU scenario, integrated scenario considers (1) a 10% improvement in the fuel economy of the vehicles, (2) a 10% reduction in the emission intensity of electricity generation, (3) a 30% increase in the transportation cost, and (4) a 15% reduction in the trip lengths. Under the BAU scenario, the SD model shows that energy consumption per capita from passenger trips will increase from 183 L of oil equivalent in 2016 to 315 L of oil equivalent in 2025 while the associated CO2 emissions per capita will increase from 460 kg in 2016 to 807 kg in 2025. To combat this dramatic growth, the findings indicate that the ambitious integrated scenario achieves the lowest energy consumption and CO2 emissions by offering a 33.5% expected reduction in total energy consumption and a 32.8% expected reduction in total CO2 emissions.

Features and drivers for energy-related carbon emissions in mega city: The case of Guangzhou, China based on an extended LMDI model.

Based on the apparent energy consumption data, a systematic and comprehensive city-level total carbon accounting approach was established and applied in Guangzhou, China. A newly extended LMDI method based on the Kaya identity was adopted to examine the main drivers for the carbon emissions increments both at the industrial sector and the residential sector. Research results are listed as follow: (1) Carbon emissions embodied in the imported electricity played a significant important role in emissions mitigation in Guangzhou. (2) The influences and impacts of various driving factors on industrial and residential carbon emissions are different in the three different development periods, namely, the 10th five-year plan period (2003–2005), the 11th five-year plan period (2005–2010), and the 12th five-year plan period (2010–2013). The main reasons underlying these influencing mechanisms were different policy measures announced by the central and local government during the different five-year plan periods. (3) The affluence effect (g-effect) was the dominant positive effect in driving emissions increase, while the energy intensity effect of production (e-effect-Production), the economic structure effect (s-effect) and the carbon intensity effect of production (f-effect-Production) were the main contributing factors suppressing emissions growth at the industrial sector. (4) The affluence effect of urban (g-effect-AUI) was the most dominant positive driving factor on emissions increment, while the energy intensity effect of urban (e-effect-Urban) played the most important role in curbing emissions growth at the residential sector.

Transforming urban energy systems: The role of local governments’ regional energy master plan

Recent estimates suggest that cities account for approximately three-quarters of global energy-related carbon emissions. At the Paris climate agreement in 2015, major cities were identified as a key player to decarbonise the energy generation sector. Australia is one of the most urbanised countries in the world, within which the local government sector has a significant capacity to accelerate a transition to clean energy generation. Through a case study of a renewable energy master plan involving five urban-councils in Sydney, this study uncovers the underlying drivers and emerging project challenges behind a cooperative joint-council approach. Social and environmental responsibility was found to be the primary motivator to develop a regional renewable energy master plan. Leveraging the collective bargaining-power through a regional joint-procurement process was an equally significant motivation. The study also revealed five major challenges. Most projects are reliant on some degree of funding from commonwealth and state government grants which are often associated with political uncertainty and a lack of continuity in programs and funding. Different positions exist as to the preferred financing options for solar-systems among councils. The previous success of a collective bargaining by councils that secured below-market prices supply-contracts of fossil-based electricity, paradoxically, reduced the financial viability of the current renewable energy project. The installable roof-space of some sites was over-estimated when not considering an on-site demand and low feed-in-tariff dynamics. Insolvency risk of renewable energy suppliers was identified as a factor that could impact on the long-term success of the project. Looking forward there are opportunities for progressing clean energy generation transformation through collective action by local government. Revolving clean energy funds can be used to establish and support renewable energy and energy efficiency projects. Regional collaboration can draw on existing technical expertise and institutional and political learnings. Power supply arrangements between councils should be supported through innovative governance and financial arrangements. Combined, these initiatives should be framed to distribute risks, lessen uncertainty and support innovation.

Spatial cluster and Heterogeneity Research on Energy-Related Carbon Emissions in Districts and Counties of Guangdong Province

Energy-related carbon emissions in districts and counties of Guangdong province from 2005 to 2016 are researched based on spatial econometrics method in this article, and significance cluster area and heterogeneity area are precise pinpointed. Conclusions are as follows: (1) total carbon emissions and per capita carbon emissions exist significance global spatial autocorrelation in the year 2005-2016, and formed significance high-high cluster area in districts and counties of Guangzhou city, Shenzhen city and Dongguan city. It also formed three significance low-low cluster areas in districts and counties of eastern, western and northern of Guangdong province. Low-high heterogeneity area and high -low heterogeneity area often appears in the scope of high-high cluster area and low-low cluster area. (2)Carbon emission intensity not exist significance global spatial autocorrelation, but exist significance cluster area and heterogeneity area in the ecological development areas of eastern, western and northern of Guangdong province. In the end, the paper puts forward the regional and detailed policy recommendations for efficient carbon emission reduction for each cluster type region: carbon high-high cluster areas are priority reduce emissions area, heighten energy saving technology and optimize industrial structure are two grippers to reduce emissions. Low - low carbon emissions concentrated area in western of Guangdong should primarily develop high and new technology industry. Low low carbon emissions concentrated areas and high - high carbon emissions intensity concentrated area for eastern and northern of Guangdong province should try hard to wins ecological compensation at the same time focus on developing ecological tourism.

Explore the influence mechanism of carbon emissions decline on energy intensity with two-layer factor decomposition method in Beijing-Tianjin-Hebei region.

Understanding the intrinsic mechanism behind changes on energy intensity provides insights about reducing carbon emissions and promoting the sustainable development of Beijing-Tianjin-Hebei (BTH) region. Although various studies have found a causal relationship between energy intensity and energy-related carbon emissions, the internal mechanisms are still unclear. This paper presents a comprehensive analysis of the impact of energy intensity on carbon emissions from 2005 to 2015. With an association established between logarithmic mean Divisia index (LMDI) and generalized Fisher index (GFI), two-layer factor decomposition model is proposed to explore the factor analysis in-depth. (1) LMDI method proves that energy intensity is the main contributor that reduces carbon emissions in BTH. (2) GFI model further decomposes energy intensity into five effects, namely energy substitution, technology progress, labor productivity, capital substitution, and labor-capital resources allocation. (3) The results reveal that the effect of capital-energy substitution in declining energy intensity surpasses technology progress. (4) Energy-labor substitution has increased energy intensity, while energy-energy substitution is negligible. For the coordinate development of BTH, the government should aim at energy intensity and attach importance to encouraging entrepreneurship, accelerating the construction of carbon trading market, allocating resources rationally, and guiding the capital flow into energy-efficient direction.

Spatial Spillover and the Influencing Factors Relating to Provincial Carbon Emissions in China Based on the Spatial Panel Data Model

From the perspective of spatial geography, this paper verifies the spatial dependence of China’s provincial carbon emissions. The contribution of impact factors with different fields of view to carbon emissions’ growth is estimated based on the spatial panel data model, t. The study found that during 2000–2015, China’s energy-related carbon emissions in the provinces were dependent on the spatial, and the spatial spillover effect of carbon emissions and its influencing factors in the neighboring provinces are obvious. It was also found that economic growth, industrial structure, financial development, and urbanization rates are positive, and the effect of the population and technological progress on reducing carbon emissions is significant. The effect of source price, export dependence, and fiscal decentralization on carbon emissions’ growth did not pass a significance test. In the formulation of carbon emission-related policies and development plans, the government must consider the effect of the influencing factors affecting the carbon emissions in the adjacent area and combine the carbon emissions and spatial spillover effect of the related factors in order to reduce carbon emissions in the time dimension and the spatial dimension of China as a whole.

Decoupling effect and sectoral attribution analysis of industrial energy-related carbon emissions in Xinjiang, China

As the gateway of “the Belt and Road”, it is of great significance for Xinjiang to fulfill the carbon reduction target without compromising the steady socio-economic development. A comprehensive understanding of the decoupling relationships between industrial growth and carbon emissions as well as its driving factors is helpful to make targeted recommendations. In this paper, decoupling analysis, index decomposition analysis, and attribution analysis were applied to analyze the decoupling effect, driving factors, and contributions of sub-sectors to each driving factor, respectively. Some conclusions were drawn. (1) Xinjiang’s decoupling relationships between industrial growth and carbon emissions experienced three stages, i.e., “negative decoupling, weak decoupling, and negative decoupling”. Xinjiang’s industrial sector was in relatively unsustainable period after 2008. (2) From 2000 to 2014, energy intensity promoted the decoupling process, and industrial structure and energy structure were the main factors which inhibited the decoupling process. But after 2008, all driving factors showed negative influences. (3) After 2008, fuel processing, textile, and mining and quarrying were primarily responsible for the energy intensity’s negative influence; fuel processing was primarily responsible for energy intensity’s negative influence; production and supply, smelting and pressing of metals, and chemicals were primarily responsible for industrial structure’s negative influence. (4) This paper suggests that Xinjiang should make targeted carbon reduction policies at sub-sector level.

Is carbon emission decline caused by economic decline? Empirical evidence from Russia

Russia’s energy-related carbon emission decreased by roughly 30% between 1992 and 2017. Previous studies reported that economic recession led to carbon emission reduction in Russia during 1990s. This paper aims to examine whether the economic recession remains to lead to a decline in Russia’s carbon emission for 1992–2017. The results show that not economic recession, but improving energy efficiency is the most significant contributor to decreasing Russia’s carbon emission from 1992 to 2017. Economic recession is the major contributor to the decrease in Russian carbon emission only before the new century and then reversed to the leading contributor to the increase in carbon emission. This research also finds that a shift to less carbon-intensive fuel and decrease in population also contribute to offsetting carbon emission in Russia. Thus, this research argues that the cause for the decline in Russia’s carbon emission for 1992–2017 is not economic recession. Indeed, Russia’s economic activity and change in carbon emission have been delinked since the new century. It can be concluded that the reduction in Russia’s carbon emission during 1992–2017 arises from a combination of improving energy efficiency, a shift to less carbon-intensive fuel, and decrease in population.

Prospective on energy related carbon emissions peak integrating optimized intelligent algorithm with dry process technique application for China's cement industry

Global climate change is a significant environmental problem. A major trigger of climate change is the excess carbon emissions. Based on 44 scenarios in the second generation of new dry cement technology systems, this paper establishes IPSO-BP model to forecast the carbon emissions peak of China‘s cement industry for 2016–2050 years. The results indicate that China's cement industry only implements capacity reduction plans and the second generation of new dry cement technology systems, so that carbon emissions can reach the peak before 2030. It is up to 19 years ahead of the carbon emissions peak of the basic scenario and the carbon emissions peak is reduced by 38 Mt. Moreover, this paper analyzes the technical combination of the earliest carbon emissions and the lowest carbon emissions. As for the earliest carbon emissions technical combination, China's cement industry carbon emissions will peak at 789.95 Mt in 2021. According to the lowest carbon emissions technical combination, China's cement industry carbon emissions will peak at 742.37 Mt in 2025. Accordingly, the conclusions will be helpful for making carbon emissions reduction policies for China's cement industry.

The Influence of Building Envelop Materials on its Life Cycle Performance: A Case Study of Educational Building in Thailand

The analysis of life cycle energy (LCE) and life cycle carbon (LCC) of building were performed in this study in order to identify the solutions for reducing energy-related carbon emission throughout building life time. The influence factors associated with building envelop materials (wall, insulation, window, window-to-wall ratio) were evaluated. The result showed that operation phase contributed a vast majority (&gt;90%) of LCE and LCC. Only 4% emissions saving could be achieved if autoclaved aerated concrete block, cellulose insulation and triple glazing were implemented with WWR of 0.17. The finding suggested that reducing carbon emission should not only be prioritized through use of high energy efficient materials/technologies but should also integrate energy saving measures since energy demand in tropical country is quite high for cooling building. In addition, increasing a possibility and feasibility for supplying renewable energy should be further investigated importunately.

Spatial variations of PM2.5 in Chinese cities for the joint impacts of human activities and natural conditions: A global and local regression perspective

Fine particulate matter (PM2.5) concentrations are mainly influenced by human activities and natural conditions, yet how these impacts are driven under these two circumstances is not well understood. Identifying the spatial characteristics and the potential determinants of PM2.5 variations from the joint perspectives, can provide insights into particulate pollution control. Due to the limited observations for PM2.5, here a timely structure adaptive modeling method was employed in order to estimate PM2.5 concentration levels in China in 2014. Then global and local regression approaches were combined in order to identify the joint impacts of natural conditions (elevation, vegetation, precipitation, and temperature) and eight anthropogenic factors (including urban sprawl and economic structure) on PM2.5 concentrations. Results indicated an annual mean concentration of 69.7 μg/m3 of PM2.5 in China, with significant differences being observed across space. More than 70% of Chinese cities were found to exceed Grade II of the Chinese National Ambient Air Quality Standard, with the highest levels in the Sichuan Basin and the North China Plain. Global regression results, showing the relative influence of the twelve factors on variations in PM2.5 levels, indicate that elevation, vegetation, precipitation, temperature, and per capita GDP all have potentially offsetting effects in relation to increasing PM2.5 levels that are being driven by industrial structure and energy-related carbon emissions. If China persists in its development pattern, industrialization and fossil energy consumption will inevitably increase a development which will lead to higher PM2.5 concentrations according to the results of the analysis. The geographically weighted regression results showed that the relationship between the factors studied and PM2.5 concentrations is spatially heterogeneous at the local geographic level. Elevation (77.3%) and urban sprawl (57.4%) had the most significant impacts on PM2.5 concentrations. Findings from this research can shed new light on the multiple mechanisms underlying spatial variations in PM2.5 throughout China and provide insights useful to particulate pollution control in China.