Growth Of CO2 Emissions Research Articles

The causality between human capital, energy consumption, co2 emission and economic growth: evidence from Bangladesh

The causality between human capital, energy consumption, co2 emission and economic growth: evidence from Bangladesh

Evaluation of carbon emission efficiency in China's airlines

Abstract Aviation CO2 emissions are increasing along with traffic growth, and expected technological improvements are not enough to reduce this continuous growth. In response to the rapid growth in air transport CO2 emissions, International Civil Aviation Organization has proposed the “Carbon Neutral Growth from 2020″ strategy to reduce aviation carbon emissions. Using 13 Chinese airlines data from 2009 to 2013, this study investigates the static and dynamic efficiencies of carbon emissions through the global slack based measure (GSBM) model and the Global Malmquist-Luenberger productivity index (GML). The results indicate that, first, the static efficiency of carbon emissions in airlines showed an inverted U-shaped trend during the inspection period. Second, the average values of dynamic carbon emissions efficiency of China's airlines in 2009–2013 are 1.098, 1.157, 0.995, and 0.950 respectively. Third, the reasons for the changes in the dynamic efficiency of each airline are different. Monitoring the carbon emissions efficiency of airlines may help the government to develop targeted carbon-reduction policies.

The driving factors of energy-related CO2 emission growth in Malaysia: The LMDI decomposition method based on energy allocation analysis

Malaysia is a typical Southeast Asian country that is a dynamic part of the global growth of energy-related CO2 (carbon dioxide) emissions, but little research exists on the driving factors of its energy-related CO2 emission growth. Most of the related publications have considered only the effect of the change of economic indicators using econometric methods, and seldom have they considered the technical driving factors from the perspective of energy systems. In this study, a methodology called the logarithmic mean Divisia index (LMDI) decomposition method based on energy allocation analysis was applied to define the contributions of technical driving factors related to the growth of CO2 emissions in Malaysia during the periods 1978–1990, 1990–2002, and 2002–2014. The technical driving factors include end-use energy structure, electricity generation efficiency, and fuel-mix in electricity generation. The results indicate that, although the population, GDP per capita and energy intensity are still the main driving factors influencing the changes of energy-related CO2 emissions in Malaysia, the influence of technical driving factors is increasing from in 1978–2014. The increasing ratio of electricity in the end-use stage and the structural changes of fuel-mix in electricity generation contribute to energy-related CO2 emission growth. Meanwhile, the increasing end-use energy efficiency and electricity supply efficiency effectively slow down CO2 emissions in Malaysia. Compared with previous publications, the technical driving factors considered in this study can provide a more detailed explanation for the interaction between energy, the economy, and CO2 emissions. On the basis of an overview of Malaysia's existing policies, policy recommendations for further control of energy-related CO2 emissions in Malaysia that mainly focus on these technical factors were proposed.

Uncovering the impacts of industrial transformation on low-carbon development in the Yangtze River Delta

In China, industrial transformation have received attention as an effective means of CO2 emissions abatement. In our study, industrial transformation has a more generalized meaning including investment structure, trade structure, final demand structure, intensity structure and production structure. We built up a comprehensive analysis framework to evaluate how the industrial transformation process impact on CO2 emissions from the sectoral perspective, and applied it to the Yangtze River Delta, which is a leading region in terms of economy, industrial, and relatively low-carbon development within China. The results indicate the Yangtze River Delta has gradually developed toward an industrialization structure dominated by the service and advanced manufacturing sectors such as machinery manufacturing, leading to a slow growth in CO2 emissions during 2002–2012. The improvement of CO2 intensity and production structure in the service and construction sectors, and in machinery manufacturing particularly, has mainly led to this slow growth. Moreover, substantial CO2 emissions were avoided by imports from resource-related manufacturing and mining sectors. Within this region, the differences in improvement of CO2 intensity and production structure in machinery manufacturing, services sectors led to the gap in CO2 emissions between Shanghai and Zhejiang from 2002 to 2012. Meanwhile, swift investment, exports and inconspicuous improvement of CO2 intensity and production structure in machinery manufacturing and construction sectors widened the gap in CO2 emissions between Jiangsu and two other regions. Further, optimizing CO2 intensity and production structure is a feasible way to realize regional low-carbon development.

The influencing factors and spatial spillover effects of CO2 emissions from transportation in China

CO2 emissions from transportation (TC) are one of the main causes of global climate change. China faces particularly severe pressures and challenges in transportation carbon reduction. Based on the panel data of 30 provinces in China from 2000 to 2015, this study explored the influencing factors and spatial spillover effects of TC by estimating spatial panel data models. It found that China's TC will continue to increase in the future, because the increase in per capita gross domestic product (GDP) is the primary driving force to accelerate the growth of TC, but an increasing proportion of tertiary industry (PTI) in the national economy will help reduce the growth in emissions. Moreover, urban road density (URD) and per capita highway mileage (PHM) are the other two major factors promoting the growth of TC. In contrast, urban population density (UPD) has a negative direct impact on per capita CO2 emissions from transportation (PTC) but also has a larger positive spatial spillover effect, which indicates that these three factors should be properly planned and controlled. Meanwhile, we should actively advocate the development of urban public transport because it plays an important role on reducing TC. The conclusions provide important inspiration and a scientific basis for formulating policies to effectively curb the growth of CO2 emissions in China.

Coupled LMDI and system dynamics model for estimating urban CO2 emission mitigation potential in Shanghai, China

In this study, both the extended logarithmic mean divisia index (LMDI) model and the system dynamics (SD) model were used to explore determinants of CO2 emission change during 1995–2016 and to predict the emission mitigation potential from 2016 to 2030 in Shanghai (China). Some novel factors (e.g., private car ownership, urban travel structure, and income level) were chosen and added to the LMDI model. The combination of the LMDI and SD models might provide a new pathway for policymakers to cope with such sophisticated issues. The results showed that: (1) GDP per capita is the main positive driving force for CO2 emission growth, followed by population, average income level per capita, and car ownership per capita. Energy intensity is the main factor for carbon mitigation, followed by economic structure, residential energy intensity, and emission coefficient. (2) The additive effect of different scenarios is essential for emission control. (3) CO2 emissions and emission per capita would peak by 2025 at the level of 218.20 Mt and 8.83 t per capita, respectively. Tertiary industry and public travel model promotion, power generation structure, and primary energy structure optimization would facilitate emission mitigation in Shanghai, which could also be a reference for other similar mega-cities in developing countries.

Decomposition analysis of tourism CO2 emissions for sustainable development: A case study of China

AbstractThe tourism industry is a nonnegligible contributor to global CO2 emissions, and formulating policy around emission reduction for sustainable tourism requires understanding the drivers contributing to the growth of tourism CO2 emissions. This paper develops an alternative decomposition technique that uses data envelopment analysis (DEA) and distance functions to examine the sources of change in tourism CO2 emissions; this method offers advantages in measuring the contribution of factor inputs to CO2 emissions change and has flexible data requirements. An empirical application using provincial panel data from China's tourism industry during the 2005–2016 period is performed. The results indicate the following: The scale effect induced by economic growth in tourism was the largest factor driving CO2 emissions growth; technical change played a dominant role in reducing CO2 emissions, and technical efficiency change exerted a negligible positive effect on the CO2 emissions increment; the emissions indices associated with labour, capital, and attractive resources generated significant promoting effects on CO2 emissions growth; and the emissions index associated with energy had a minor inhibitory effect. In addition, the drivers of tourism CO2 emissions clearly varied by region and province, and three driving modes of change in provincial CO2 emissions were summarized. This study extends the application of decomposition techniques in the field of tourism studies, and our results provide valuable insights for policymakers and business entities aiming to adopt measures to curb tourism CO2 emissions and promote sustainable tourism.

Uncovering embodied CO2 flows via North-North trade - A case study of US-Germany trade.

With development of globalization, the geographic separation of production caused consumption of a region was linked to pollution emitted by another region. In this process, dramatic growth of CO2 emissions embodied in international trade attracted attention of relevant studies. Existing literatures have paid much attention on the empirical research of CO2 flows between developed and developing countries, while studies of CO2 emissions embodied in North-North trade were often overlooked. To partly fill the research gap, the study assessed CO2 emissions embodied in Germany-US trade during 2000-2015, which was viewed as an example of the empirical research on CO2 flows in North-North trade. On the basis of the estimation, we conducted a structural decomposition analysis on CO2 emissions embodied in Germany-US trade. The decomposition focused on five driving forces that dominated the change of CO2 emission flows. The result showed that Germany ran both the surplus of export CO2 emissions and surplus of trade volume with the US. The share of Germany-US export CO2 emissions to total export CO2 emissions of Germany was greater than US-Germany export CO2 emissions to total export CO2 emissions of the US, which, to some extent, indicated significance of the US in Germany's external trade was greater than importance of Germany in overseas trade of the US. Decline of the two ratios above showed a trend of transferring CO2 emissions to developing countries by the two countries. By comparison of sectoral CO2 distribution, the categories of "manufacturing" and "services" dominated most emissions in the trade. According to structural decomposition analysis, CO2 emission intensity and per capita final consumption caused most growth and reduction of emissions in the first 10 years, while in the next 5 years production structure of two countries played the most important role in change of embodied emissions. And the trend seemed to continue in the future.

Impact of FDI and R&D on China's industrial CO2 emissions reduction and trend prediction

In this paper, we investigated the impact of foreign direct investment (FDI) and research as well as development (R&D) on industrial carbon dioxide (CO2) emissions reduction. Firstly, a Panel-Corrected Standard Error (PCSE) model was established based on Chinese provincial panel data during period of 2000–2017. Secondly, a Grey Model (GM) was constructed to predict the growth trend of CO2 emissions, FDI and R&D in China from 2018 to 2030. It was deduced that: (1) FDI do bring negative effects on industrial CO2 emissions at the national level. From the perspective of regions, the coefficients of the eastern, central and western regions are all negatively correlated with CO2 emissions and pass the test of 1% significance level except the northeastern China. (2) In terms of R&D, it showed significantly improvement on CO2 emissions at the national and regional levels. Moreover, the efficiency of industrial CO2 emissions was improved by R&D with a rate of 27.2%, while the related value was increased by FDI with a rate of merely 4.3% at the national level. (3) According to the results of GM (1,1), China is expected to achieve zero growth of CO2 emissions by 2030. The growth rate of FDI gradually decreased to less than 5%, while the R&D input will always be at an annual growth rate of more than 5% for a long period of time in the future.

What precipitates growth in CO2 emissions?

Purpose The degradation of the natural habitat at the expense of economic development is a harmful growth that warrants environmental policy actions. For instance, the economic impacts of environmental pollution are quite visible in developed and developing economies, where human health is compromised by rapid economic growth and energy induced pollution. Therefore, the purpose of this study is to investigate the impact of CO2 emissions on economic development. Design/methodology/approach This paper investigates the correlation between pollutant emissions and key economic variables within the economic community of West African states (ECOWAS) region by applying fixed effects model to unbalanced time-series panel data for the period 1980-2014. This paper examines the full ECOWAS panel and sub-panels with export-and-import-dependent countries. Findings The authors argue that energy consumption (EC) and real output exert causal influences on CO2 emissions for the full ECOWAS panel and the sub-panels with export-and-import-dependent countries. Practical implications The results imply that increase in EC is the main factor that promotes economic growth in the region. Additionally, growth in EC and real output stimulates CO2 emissions growth. Originality/value Therefore, it is argued that technological innovations that increase energy efficiency through new carbon-free technologies that minimize CO2 emissions growth without impairing economic growth and development must be introduced in the region.

Generalized Method of Moment Application in Simultaneous Dynamic Panel Data Equations for Economic Growth, CO2 Emissions, and Health Expenditures Modelling

Sustainable development is one of the goals of ASEAN as a UN cooperation partner. The high level of CO2 emissions as one indicator of the decline in environmental quality in several ASEAN member countries, requiring an appropriate policy in order to achieve sustainable development targets. Based on previous research, the relationship between CO2 emissions, income, and health expenditure is not only simultaneous but also dynamic (changes in a variable spread to the current period and future periods). The relationship between these variables can be described in a system of dynamic simultaneous equations. The ten ASEAN country panel data for eight years (2008-2015) will be used by applying a simultaneous dynamic data panel model. GMM-System Estimator and GMM Arellano-Bond are used to estimate the parameters of dynamic models. Based on the accuracy of the sign with economic theory as well as from the standard error estimator produced, estimates from the GMM-System Estimator are considered better. The resulting estimates indicate a significant simultaneous influence between health expenditure and per capita income and between health expenditure and CO2 emissions. Per capita income growth affects the growth of CO2 emissions indirectly through per capita health expenditure. In addition, the lag of each variable, namely per capita income, per capita health expenditure, and CO2 emission has a positive and significant effect that indicates a long-run multiplier effect.

Investigating the airlines emission reduction through carbon trading under CNG2020 strategy via a Network Weak Disposability DEA

In response to the rapid growth in air transport CO2 emissions, International Civil Aviation Organization proposed the “Carbon Neutral Growth from 2020” strategy (CNG2020 strategy). The total CO2 emission reduction is investigated for 28 airlines during 2021–2023 under CNG2020 strategy based on the predicted data by BP neural network. A Network Weak Disposability DEA model is applied to calculate the airlines’ maximum desirable outputs when the inputs are fixed and the undesirable outputs are weakly disposed. Then carbon trading is added into the model, which can obtain the maximum desirable outputs when taking into account of carbon trading. Firstly, the total max Total Revenue of these 28 airlines is much larger than the total original Total Revenue gotten by BP neural network. Secondly, for these 28 airlines, the maximum expandable Total Revenue and Greenhouse gases emission reduction can be achieved simultaneously under CNG2020 strategy, but the Greenhouse gases emission reduction proportion is too small to satisfy the requirements of CNG2020 strategy.

Uncovering CO2 Emissions Patterns from China-Oriented International Maritime Transport: Decomposition and Decoupling Analysis

Given that most commodity transportation depends on the maritime industry, the growing economy and increasing international trade volume are expected to accelerate the development of shipping activities and thus increase associated CO2 emissions. In order to identify the driving factors of CO2 emissions from China’s international shipping and find efficient mitigation strategies, this paper first estimates the CO2 emissions and presents the CO2 emissions features from 2000 to 2017. Second, the Logarithmic Mean Divisia Index (LMDI) method is applied to decompose the changes in CO2 emissions. Finally, the decoupling index is introduced to quantitatively examine the decoupling relationship between economic growth and CO2 emissions. The factors affecting the decoupling relationship are analyzed according to the LMDI results. The results indicate that CO2 emissions in maritime transport activities have experienced rapid growth during the study period. Economic growth appears to be the principal factor driving the CO2 emissions growth, whereas the overall effects of energy intensity and the commodity structure play a significant role in inhibiting CO2 emissions. The decoupling state over the study period has experienced four decoupling stages, with a distinct tendency towards weak decoupling. Economic activity has proven to be the most significant indicator influencing the decoupling relationship during the study period.

CO2 emissions embodied in China’s export

ABSTRACTThe reduction of CO2 emissions embodied in export will have an important role for China to achieve its CO2 emissions peaking target. In this study, we use input–output analysis to examine the embodied CO2 emissions in China’s export to its top largest trading countries from 2008 to 2015. We find that China’s exported CO2 emissions peaked in 2008. More than 70% of embodied CO2 emission were exported to only 20 countries, and CO2 emissions exported to developing countries have been increasing. High-energy consumption sectors contribute to CO2 emissions embodied in export significantly, such as Electricity, Gas and Water. We conclude that structural adjustment could be considered alongside technological improvements to curb China’s growing CO2 emissions.

IMPACT OF ENVIRONMENTAL POLICY FACTORS ON TOURISM INDUSTRY: A STUDY FROM INDONESIA OVER LAST THREE DECADES

The purpose of the present study analysis is to empirically examine the impact of various environmental factors on the Tourism industry in Indonesia. To achieve this objective, data is collected from World Development Indicators during the time of 1996-2016 under the title of environmental factors as defined by the experts of the world bank. Atime of 22 years has been finalized for the predictors of tourism industry like forest area (FA), population growth, energy use, agriculture, forestry and fishing along with CO2 emissions. Both descriptive and regression analysis techniques have been applied while adding the lagged predictors of both tourism industry and environmental factors. Eight regression models have been developed and results are generated through statistical tools like STATA-14. Findings of the study indicate that FA and population growth are the significant determinants of the tourism industry. Among the lagged predictors, international tourism (lagged), Nitrous oxide emissions, and lagged values of FA are significantly affecting international tourism receipt (ITR) in the region of Indonesia. For the 2nd proxy; tourism expenditures, the key determinants under model 5–8 are, international tourism (lagged), Nitrous oxide emissions (lagged), CO2 emission (lagged) and population growth. As per the practical implications, this study is very much useful for both the industry experts and policymakers at macro-level. Considering the set of factors as explained in the present study will provide new insight regarding sustainable development through environmental factors of the tourism industry. However, the key limitations are the lack of advance time series investigation, ignoring the impact of macroeconomic indicators and cross region analysis.

Türk Cumhuriyetleri’nde CO² Emisyonu ve Ekonomik Büyüme İlişkisi: Panel Nedensellik Analizi

Bu çalışmada Türk Cumhuriyetleri’nden Azerbaycan, Kazakistan, Kırgızistan, Tacikistan, Türkmenistan ve Özbekistan’da CO² emisyonu ve ekonomik büyüme ilişkisi 1992-2014 dönemi verileri ile araştırılmaktadır. Karbon emisyonu olarak ülkelerin kişi başı karbon emisyon oranları, gaz ve akaryakıt tüketiminden kaynaklanan karbon emisyon verileri kullanılmıştır. Yatay kesit bağımlılık ve homojenlik testi sonuçlarına göre durağanlık için ikinci kuşak panel birim kök testleri tercih edilirken Panelin parametrelerinin heterojen yapıda olmasından dolayı Dumitrescu ve Hurlin(2012) panel nedensellik testi kullanılmıştır. Elde edilen sonuçlara göre söz konusu ülkelerde CO² emisyonu ve ekonomik büyüme arasında karşılıklı bir nedensellik ilişkisi olduğu tespit edilmiştir. Ayrıca gaz ve akaryakıt gibi enerji tüketiminden kaynaklanan karbon emisyonlarından ekonomik büyümeye doğru bir nedensellik ilişkisi de tespit edilmiştir.

Energy-related CO2 emission peaking target and pathways for China's city: A case study of Baoding City

The prediction of CO2 emissions peak and its time for Baoding, which is one of the first pilot cities of “China's low-carbon city development project”, is important for other cities to formulate low-carbon policies. This study first analyzes the main influencing factors of Baoding's emissions, and then predicts its CO2 emissions peak and its time under different scenarios. Results show that (1) CO2 emissions from industrial production account for more than 80% of the total emissions, and those from six energy-consuming industries account for more than 40% of the total emissions in the city. (2) Economic growth has significantly contributed to the growth of CO2 emissions, whereas industrial structure and population growth have little contributions; energy intensity is the main negative factor for emissions growth, whereas the inhibiting effect of energy structure is not evident. (3)Based on the back propagation (BP) model, the city's CO2 emissions peak will be 59.74 million tons in 2024 under the low-speed and low-carbon scenario; under the moderate-speed and benchmark scenario, it will continue to grow and reach 63.06 million tons in 2040; under high-speed and high-carbon scenario, the city's CO2 emissions will reach 80.18 million tons in 2040. This study provides a reference and experience for other cities in developing strategies to achieve the carbon emission peak, which has important practical significance for China to achieve peak carbon emissions as soon as possible.

Dynamic analysis of carbon dioxide emissions in China's petroleum refining and coking industry

As one of the energy-intensive industries, the CO2 emissions of China's petroleum refining and coking industry (PRCI) put significant pressure on China's emission reduction target. Considering this, the main purpose of this paper is to clarify the determinants of emission growth in China's PRCI and predict the emissions change trend in future. Based on an extended Kaya identity, this paper dynamically analyzed the impacts of investment scale, investment efficiency, energy intensity, energy structure and carbon dioxide emission factor on CO2 emissions from China's PRCI. Then, the possible CO2 emissions of China's PRCI in 2030 were dynamically predicted based on the application of probabilistic scenario analysis. The main findings include: (1) during 2000–2016, China's PRCI suffered 130.82 million tons CO2 emissions growth, investment scale expansion was the principal contributor stimulating CO2 emissions. Fortunately, the promoting effects exerted from this factor showed a downtrend along with the decreasing fixed-asset investment growth rate since China's economy has entered a new normal. (2) During the entire study period, energy structure adjustment brought about a mild increase in CO2 emissions rather than taking an active role in emission abatement. (3) Investment efficiency and energy intensity were two crucial inhibitors, followed by the carbon dioxide emission factor. However, the inhibiting effects resulting from these factors cannot completely counteract the promoting effects in most cases. (4). Scenario analysis indicates that the mitigation pathway of CO2 emissions in China's PRCI include properly curbing the fixed-asset investment, increasing the green investment for technology and equipment, paying more attention to the R&D of energy saving technology in PRCI and its downstream industry, and facilitating the use of clean electricity.

The analysis of CO2 emissions and reduction potential in china's production and supply of electric and heat power industry: A case study based on the LMDI method

The Production and Supply of Electric and Heat Power Industry is an important energy supply sector in China. In this article, we apply the Logarithmic Mean Divisia Index decomposition method to analyze the carbon emissions of China's electricity and thermal supply industry from 2003 to 2012. Under different scenarios, this study also forecasts the carbon emissions and calculates reduction potential of The Production and Supply of Electric and Heat Power Industry. The results show the economic output effect and energy intensity effect promote the sector's CO2 emissions growth, however the energy structure effect decrease the sector's carbon emissions. At the end of 2030, the accumulated emission reduction will be 4160.84 Mt in moderate reduction scenario and 11,593.27 Mt in strong reduction scenario. If all the indexes maintain the current change rate, the industry will reach carbon emissions peak at the time of 2030, What is more, the time for emissions peak will be 2025 and 2020 in moderate reduction scenario and strong reduction scenario. Finally, the article proposes to improve energy efficiency and promote the development of clean energy to reduce the carbon emissions of the Production and Supply of Electric and Heat Power Industry. Draw more stringent environmental standards and incentives to promote energy‐saving existing technology, the application can also combine with the means of financial subsidies to encourage the companies to reduce the energy intensity of the sector or strengthen the impact of energy intensity effect on the change of carbon emissions, promote the industry early peak. © 2019 American Institute of Chemical Engineers Environ Prog, 38:e13146, 2019

Does the De-Industrialization and Tertiarization Process De-Carbonize Emissions in Asian Countries?

To date, slowdown trends in the global 𝐶𝑂2 emissions registered in 2012 to 2015 argue due to the transformation from an industrial based on a less-energy intensive services oriented. Nevertheless, inconsistent and insufficient evidence of the role of sectoral composition in influencing the carbon emissions is a serious academic and policy gaps that were overlooked. Thus, due the fact that Asian countries experiences a dramatic shift in phases of economic structure, this study aim to investigate whether the de-industrialization and tertiarization process might be the solution to the carbon emissions in Asian countries. As the structural heterogeneity exists widely across countries in Asian countries, this paper employs Stochastic Impact by Regression on Population, Affluence, Technology (STRIPAT) model as its analytical framework and estimate using the Dynamic Common Correlated Effects (DCCE) estimator to address the heterogeneity, cross-section dependence, and dynamic nature of carbon emissions. In sum, this study found that the de-industrialization moderating the 𝐶𝑂2 emissions in Asian countries, while the expansion of service sector bound to reduce the 𝐶𝑂2 emissions as presented in quadratic functional. Nevertheless, an N-shape relationship observe in cubic functional, thus there is no guarantee that long-term levels of pollution emissions will continue to fall as countries shifting to services-based economy. The finding of this study is important not only in term a value-added of current knowledge, but also in assisting the development of strategic planning and better sustainable policy to combat the growth of 𝐶𝑂2 emissions.