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Reducing energy intensity and coping with climate change are important policy issues that face all countries. This study examines the impact of business cycles (recessions and expansions) on energy intensity in sixteen emerging countries in 1990–2014. We find that during periods of economic expansion (recessions), business cycles reduce (increase) energy intensity. We also study the moderating effect of foreign direct investment (FDI) between business cycles and energy intensity and find that FDI lowers (raises) energy intensity in countries with high (low) level economic development. In addition, this study finds a nonlinear relationship between business cycles and energy intensity. Policy implications of the findings are discussed.

Efficiency has become the basis for boosting the environmental satisfaction and economic performance of gardens. The major goal of this research is to optimize the date production process by minimizing the quantity of inputs in the process using parametric and non-parametric approaches, as well as to investigate the amount of energy consumption and economic indicators in the date palm production in Iran's Khuzestan region. For this purpose, data envelopment analysis (DEA: CRS & VRS and Anderson-Peterson) was used to optimize energy efficiency and present the ideal pattern of input consumption. Also, the transcendental production function was developed to represent the relationship between energy consumption and product performance. The outcomes revealed that the average entire energy expenditure in date palm production was 35.52 GJ ha−1. Among inputs, irrigation, diesel and manure have contributed to the highest energy consumption and their contribution was calculated to be 43.92%, 32.86% and 9.49%, respectively. The average of energy ratio, energy productivity, energy intensity and net energy gain were equal to 3.66, 0.39 kg MJ−1, 2.70 MJ kg−1 and 91290.66 MJ ha−1, respectively. Moreover, direct and indirect energy, as well as renewable and non-renewable energy, were computed at 76.64%, 26.46%, 56.28%, and 43.72%, respectively. DEA approach showed that there is potential to prompt sustainability, and based on the constant return to scale 10.15% of input energy is able to be alleviated. The coefficient determination of the transcendental production function model was obtained as 0.92 and gross return, benefit-cost ratio, economic productivity, payback period, invest return rate, percentage of production at the break-even point and profit per kg were calculated as 3550.63 $ ha−1, 0.44, 1.67, 2.23 year, 0.44, 2.26 and 0.26 $ kg−1, respectively. To save energy, it is suggested that solar energy be used instead of diesel fuel to extract water, and that simultaneous cultivation be implemented to limit the usage of chemical fertilizers.

Made available in DSpace on 2022-04-29T08:40:53Z (GMT). No. of bitstreams: 0 Previous issue date: 2022-04-01 Trường Đại học Kinh tế - Luật, Đại học Quốc gia Thành phố Hồ Chí Minh This study aims to unveil the heterogeneous impacts of import product diversification and institutional factors for achieving energy efficiency in Organization for Economic Co-operation and Development (OECD) countries. In doing so, the study employs robust econometric techniques such as Fully modified ordinary least squares (FMOLS) co-integration, pooled Ordinary Least Squares (OLS), Feasible Generalized Least Squares (FGLS), pool mean group regressions, fixed effects, and random effects for panel data from 1990 to 2015 for selected OECD countries. The detailed empirical outcomes suggest that import product diversification is conducive to reducing the energy and carbon intensity (improvement in energy efficiency) in OECD countries. The empirical conclusions provide various guidelines to achieve cleaner and greener growth and align with various Sustainable Development Goals (SDG 7: Affordable and clean energy, SDG 9: Industry, innovation, and infrastructure, and SDG 13: Climate action) of OECD countries. The paper elaborates fruitful policy suggestions regarding the diversification of imports and energy use-carbon emission-nexus for the OECD member nations. Based on the findings, policymakers and environmental scientists should strengthen the trade-energy and import portfolio policies to attain energy efficiency. Department of Economics Suleyman Demirel University Department of Economics Federal University of Ouro Preto (DEECO-UFOP), Rua do Catete 166 – Centro, Mariana Innovation Economics Institute of Economics University of Hohenheim Department of Production Engineering School of Engineering of Bauru Campus Bauru São Paulo State University (UNESP) S. P. Jain Institute of Management and Research (SPJIMR) UEH Institute of Innovation (UII) University of Economics Ho Chi Minh City (UEH), 59C Nguyen Dinh Chieu Street, District 3, Vietnam Department of Production Engineering School of Engineering of Bauru Campus Bauru São Paulo State University (UNESP) Trường Đại học Kinh tế - Luật, Đại học Quốc gia Thành phố Hồ Chí Minh: 2022-03-11-0887

The article deals with the issue of mechanization of the process of ecological and meliorative tillage with the turnover of the reservoir, improving the quality and reducing the energy intensity of the arable unit. The state of modern research of ecological and meliorative methods of soil treatment is studied. The modernization of the plow was carried out and a variant of a new design was proposed in the form of chisel working bodies instead of pre-lugs to the coulter plow. At the same time, the improvement of the methods of dump plowing does not violate the main principle of the plow body – falling off and turning the formation into an uncovered neighboring furrow. A review of the theoretical prerequisites for the substantiation of the design parameters of chisel working bodies is carried out. The optimal parameters of the chisel chisel working bodies operation process are determined, depending on the type of chisel and its surface area. Scientific research was carried out according to the strategy of machine-technological modernization of agriculture in Russia, aimed at increasing labor productivity growth, which does not exclude technological re-equipment of agriculture.

High level of carbon emissions is still a policy agenda because of its detrimental effect on environment. CO2 emissions are growing quickly in emerging countries with the high energy consumption, industrial activities, and rapid globalization. So, it is important to examine the roles of emission-inducing factors in these countries. To this end, this research investigates the dynamic effects of energy intensity, renewable energy, industrialization, urbanization and international trade on carbon intensity in the 25 largest emerging economies of the world. The study is based on the data period of 1990–2018, where the Pool Mean Group (PMG) estimation technique of the panel Autoregressive Distributed Lag (ARDL) approach is used, along with different diagnostic tests. The findings are, in the long-run, carbon intensity is increased by energy intensity and industrialization, and decreased by renewable energy use and urbanization. An increase of one unit in energy intensity and industrialization increased carbon intensity by 2.616 unit and 0.002 unit, respectively. Conversely, carbon intensity reduced by 0.003 unit with an increase of one unit in renewable energy and urbanization. In the short-run, carbon intensity is positively associated with energy intensity only with a coefficient value of 0.811. Therefore, an increase in renewable energy production and use, and enhancement of green technology for industrial production can be viable options for carbon reduction.

Examining the dynamics of technological progress in determining energy intensity not only helps in assessing the relative strength of contributing factors, but also beneficial in designing effective energy policies. The effect of technology on energy intensity was widely discussed, as a whole, without narrowing down to the energy field and considering the technological absorption capacity as well as the heterogeneity. In view of the importance of sustainability in Guangdong province to the Greater Bay Area, this study focused on energy-specific technological progress and investigated the effect of energy-specific technology on energy intensity using a panel dataset covering cities in Guangdong Province for 2005–2017. The empirical evidence shows that energy-specific technology is beneficial in reducing the energy intensity. However, the energy intensity reduction effect is primarily from energy-saving technology rather than the alternative energy technology. From the sources of energy technology, enterprises, rather than from higher education institutions and independent research institutions, are more effective in cutting the energy intensity. In addition, utility-type energy-specific technology shows a stronger reduction effect on energy intensity compared to creation-type. Finally, the study concludes that technological absorption capacity is an important determinant for the effectiveness of energy-specific technology in reducing energy intensity.

This study measures the resource efficiency of a basic oxygen steelmaking plant and develops visual maps of its resource use using raw energy and material data extracted directly from the control system. Resource efficiency is measured in units of exergy and resource flows are visualised in Sankey diagrams. Both the metric and the visuals are computed in close-to-real time scales, and are presented on a daily basis over a period of two weeks. Results show the highest level of resource efficiency (73.8%, Day 11) occurs not when energy intensity is greatest (Day 14) but instead when both energy intensity and material yields are high. Combining energy and materials into a single metric – resource efficiency – is shown to provide plant managers with a clearer picture of where interventions might deliver the greatest efficiency gains and redefines the concept of best practice.

The challenge of global warming requires deep decarbonization of the electricity system, which can be achieved with a higher share of renewables in the electricity mix. However, the driving mechanism for renewable penetration in electricity production has not been clearly revealed so far. Using the data of the European Union (EU) during 2001–2017, this paper aims to investigate the spatial-temporal heterogeneity and dynamic evolution mechanism of renewable electricity penetration. To this end, from the investment-production-consumption perspective, renewable electricity generation is decomposed into seven factors through temporal and spatial factor decomposition methods. Then, this paper integrates the factor decomposition results into a coupling elasticity analysis to study the dynamic evolution mechanism of renewable penetration in electricity production. The results are as follows. (1) Investment structure contributes the most to increasing renewable electricity generation, while the energy intensity effect and renewable generation-to-capacity ratio effect are the main reasons for the reduction in renewable electricity generation. The effect of technological structure is minor. (2) The economic scale effect and the investment structure effect are dominating factors accounting for the regional difference of renewable power generation between the EU countries and the benchmark region. (3) The progress of renewable electricity penetration accelerates over time for the EU, western EU, and eastern EU. The Western EU has realized remarkable progress in facilitating renewable electricity penetration during 2009–2017. (4) On the whole, the investment effect makes the largest contribution to promoting renewable electricity penetration, while the production effect is the major inhibiting factor, showing that there is considerable potential in renewable generation efficiency improvement in the EU. This paper provides insightful implications for energy policies aimed at the shift towards a low-carbon electricity system in the EU, which also gives a reference for other economies.

The paper presents the environmental sustainability of the Nigeria transport sector (NTPS) through the decomposition and decoupling analysis from 1988–2019. The study’s objective is to determine ways of saving energy in the NTPS and reduce carbon emission for a sustainable environment. The approach was based on the Logarithmic Mean Divisa Index (LMDI) and the Tapio approach, built on Kaya extended identity. Five decoupling indicators were considered based on the four energy carriers consumed in the NTPS. The indicators include economic activity, energy structure, economic structure, population and energy intensity. The results identified three decoupling states, weak negative decoupling, weak decoupling and strong decoupling. The energy intensity, economic activity, population and energy structure prevented decoupling during the study period, while the economic structure factor promoted decoupling. The overall impact of carbon emissions from NTPS was calculated at 44.45 million tonnes of CO2. The study suggests frameworks that will support policy makers to formulate broad base policies for environmental sustainability.

This paper studies the impact of financial structure on energy conservation and emission reduction by using Chinese provincial data from 2000 to 2017. We find that the market-based financial structure can significantly reduce energy intensity and carbon emission intensity, especially in the Midwest and after the 2008 financial crisis. Meanwhile, technological progress and industrial structure upgrading are important influencing channels. In addition, the marginal impact of financial structure on energy conservation and emission reduction is in the dynamic evolution process. The study suggests that the financial structure is of great significance for energy conservation and emission reduction in China.

Energy quota trading (EQT) is a market-driven method to reduce distortions in energy allocation. In 2016, an EQT policy was proposed in China. However, the economic and environmental effects of this new policy remain unclear. To study these problems, this paper conducts an empirical analysis using data envelopment analysis (DEA) and index decomposition analysis (IDA). Following a simulation of the optimal energy input and maximum output in three sectors of the 30 Chinese provinces that have adopted China's EQT policy, the main conclusions are as follows: (1) The energy consumption of the industrial sectors is excessive, yet the primary and service sectors need further energy input, while there is vast economic potential in China's western provinces and the service sectors. (2) The optimal energy intensity effect generated by the trading policy is the largest impetus of carbon intensity reduction in China, but the obstacles to carbon intensity reduction are different in each region. (3) EQT has the potential to drive a significant decline in carbon intensity in China, resulting in a simulated average annual decline of approximately 4.09% from 2005 to 2016.

Based on China's energy consumption structure, the reduction of energy intensity is conducive to the realization of China's carbon neutrality goal, and technological innovation is the core driving force for reducing energy intensity. This paper utilizes the statistical data of industrial enterprises in China during the period of 2011–2018 to explore the technological innovation value chain efficiency under the background of the new development stage. This study divides the innovation value chain into technological innovation stage and the economic conversion stage. The network slacks-based measure (NSBM) approach is applied to calculate the two different stages’ efficiency. LSDV is utilized to analyze the influence mechanism of technological innovation factors on industrial energy intensity. The main results are as follows: first, the development trend of technological innovation efficiency (TIE) and the economic conversion efficiency (ECE) has heterogeneous effect across economic regions. The comprehensive development trend of innovation value chain in central China is the best among the four economic regions. Second, the effect of TIE on industrial energy intensity is negative, while the ECE is positive related to industrial energy intensity. In terms of technology spillover effect, the impact of import and FDI on industrial energy intensity is negative, respectively. The relationship between export and energy intensity is positive. The energy price has the negative relationship with industrial energy intensity, and the impact of energy price on industrial energy intensity is the largest. Through analyzing the efficiency of innovation value chain and the influence mechanism of technological innovation factors on energy intensity, this paper puts forwards relevant countermeasures and suggestions for effectively reducing industrial energy intensity and promoting high-quality industrial sustainable development. In addition, relevant theoretical research is provided for the sustainable green development.

Green building studies generally focus on singular performance aspects (e.g., energy, waste, water, indoor environment) with few tackling the relationships between each other, particularly the relationship between indoor environmental quality (IEQ) and building energy consumption. This study aims to explore the relationship between IEQ performance and energy consumption in National Australian Built Environment Rating System (NABERS) certified buildings. A verified climate normalization factor was localized to standardize energy use intensity in buildings from different climate zones of Australia. The normalized energy use intensity (NEUI) was calculated for all office buildings and correlated with their NABERS Energy and IE rating scores. Multivariate linear regression results reveal that one unit increase in NABERS Energy rating score and IE score can reduce NEUI by 21.98 kWh/m2 and 9.88 kWh/m2 per annum, respectively. Also, this study develops an Energy and Indoor Environment Index to benchmark the energy and IEQ performance of Australian office building. Buildings with excellent NABERS Energy and IE ratings (scores equal to/higher than 5) have been classified as high-performance NABERS buildings (HNBs) and the rest as low-performance NABERS buildings (LNBs). A comparison between 49 HNBs and 48 LNBs demonstrates that, on average, HNBs can deliver 12.6% better indoor environment quality with 35.9% less energy consumption than LNBs. In contrast, many LNBs either use excessive energy to provide a sufficient IEQ, or sacrifice IEQ to reduce energy costs and/or achieve a high NABERS Energy rating.

Large space buildings, such as airport terminal buildings, consume high energy due to the cooling demand; especially in hot arid climates; to achieve passenger thermal comfort. Evaluation of thermal environment and energy analysis inside airports is an important issue, in terms of reducing energy and controlling indoor environments. This work aims to analyze electrical energy consumption and the indoor environment of the terminal building at Assiut International Airport, Egypt as a case study. Indoor environmental conditions, and energy consumption with HVAC systems, were monitored inside five multiple functional zones within the terminal building (Departure hall, Public area, Duty-free hall, Gate, and Arrival hall) during summer (August 2017). The indoor environmental parameters monitored include: temperature, relative humidity, and illuminance. The results indicate the HVAC system consumed the largest portion (70%) of the overall building electricity demand, and the Energy Use Intensity (EUI) of ATZ terminal building is 300 kWh/m2/year. Also, indoor temperatures were found to be within the acceptable range of comfort, with a high energy consumption of 6,000,000 kWh for the year 2017. A significant reduction of monthly energy consumption was achieved based on increasing HVAC set temperature from 25 °C to 27 °C; a reduction equal to 24.5% of total energy consumption during the hot months. The results of this study help search for sustainable solutions for reducing energy consumption and improving passenger thermal comfort inside the terminal building (phase 2).

Globally, the transport sector represents more than a quarter of the total energy consumed. In Mexico, energy consumption in the road transport subsector alone represented 40.9% of the final energy required nationwide. The national goal of energy efficiency expressed in terms of energy intensity for the transport, industry, buildings, and agricultural sectors, indicates that energy intensity should decrease 1.9% per year during the 2016–2030 period. Besides, the primary strategy proposed by the government to optimize the energy consumption of freight transportation is energy efficiency. This proposal estimates the energy efficiency indicators to make a reliable assessment of the situation of the impact of energy efficiency on demand. If the goal of reducing the transport sector energy intensity is met, the intensity of freight transportation should be reduced from 0.7434 MJ / dollar in 2017 to 0.5793 MJ / dollar by 2030, which represents a reduction of 22.1%. It could represent a reduction in carbon emissions of 33.1% in the same period.

Dealing with the challenge requires appropriate planning. So as of energy intensity and the environment situation in Iran the energy systems planning is essential. About 92.4% of electricity in Iran is produced by fossil fuels in thermal power plants and has significant share for creating air pollution. Diversification of energy resources is a solution to improve the energy security and reduce environmental risks through the transition to renewable electricity generation. This study has been done by resource feasibility, scenario design and energy modelling to predict the supply and demand of electricity and aims to investigate the benefits of introducing renewables in Iran energy system. Identifying 92 potential areas, prioritizing resources, and estimating capacity by 2035 to synergize the share of renewables is the result of the feasibility section. By evaluating the potentials, the solar with 64,730 MW will have the highest power potential and the wind with 51,110 MW is the next. In modelling section, the electricity generated by renewable energies by the year 2035 is equal to 83.73 TW h in REN scenario, which is almost 11.93% of the total electricity produced but in the BAU scenario, this share is equal to 2.38%. The reduction in fuel consumption in the REN scenario compared to the BAU scenario is also equal to 82.97 MtOe, which would result in a reduction in emissions of equivalent to 221.54 MtCO2e. These are the only benefits of using 30% of the renewables potential, and if more attention is paid, it is definitely the most effective way to develop sustainable energy system.

The “National Comprehensive Demonstration City Selection of Energy Saving and Emission Reduction Fiscal Policy” (“ESER” demonstration city selection) is an important assessment-and-commendation policy to promote in-depth advancement of the construction of ecological civilization in China. Accurate evaluation of the environmental effect of the policy is of great significance to its promotion. A difference-in-differences (DID) model is used to explore the role of the “ESER” demonstration city policy in urban environmental governance. The results of empirical tests show that the policy has significantly reduced fine particulate matter (PM2.5), industrial dust, sulfur dioxide (SO2), and wastewater emissions by 39.1%, 61%, 18.3%, and 7.4%, respectively. The results of the multi-stage DID model show that the emission reduction effect of this policy increases significantly in the short term but decreases gradually in the long term. The results of the mediation effect model show that the emission reduction effect is driven mainly by increasing innovation capacity and reducing energy intensity; the effect is also more significant in cities on a larger scale, with higher levels of fiscal expenditure and financial development, more abundant human capital and higher levels of economic development. This study provides empirical support for China to further promote the assessment-and-commendation environmental policies represented by the “ESER” demonstration city policy nationwide.

In recent times, the Indian economy has enjoyed a positive economic trajectory. However, the economy remains vulnerable to domestic and geographical risk as it relates to environmental degradation. The Indian economy is reputed as one of the leading emitters of carbon dioxide ( C O 2 ) emissions globally.. In the literature, there is no consensus on the contradiction between decoupling economic growth from CO2 emission in India and other regions of the globe. By drawing on the Sustainable Development Goals (SDGs) agenda and its 2030 targets, this study examines the relationship between responsible energy consumption (SDGs 7, 12), climate issues (SDGs 13), and economic growth (SDGs 8) for the case of India. Thus, the present study seeks to investigate the implications of CO2 emissions on Indian economic growth (GDP) with a focus on the energy intensity in the country's economy. To explore the nexus between economic growth and environmental degradation in a carbon income function, openness to trade and energy use were added as additional variables to circumvent the problem of omitted variable bias. Autoregressive Distributed Lag (ARDL) with the modified Wald test of Toda Yamamoto (T-Y) were applied to annual time series data from 1975-2017. The study reveals a long-term equilibrium association among the described variables over the considered period. Furthermore, a statistically significant negative relationship is observed between C O 2 emissions and trade openness and economic growth. This study validates the energy-induced economic growth as reported by the ARDL regression. This is also corroborated by the causality analysis results, as a uni-directional relationship was observed running from energy utilization to income (GDP). Thus, the Indian government officials should not adopt conservative energy policies, as this will be detrimental for economic growth given that the economy is dependent on energy. However, based on the growing environmental consciousness around the world, there is a need to shift the energy mix in India to renewables to make uses of cleaner energy sources and create environmentally friendly ecosystems.

Driven by continuous income increases and the urgent need to improve living conditions, CO2 emissions generated from residential energy consumption have gradually become the main source of China's CO2 emissions growth. Because of both natural and social factors, China has significant CO2 emission inequalities, however, there is few literature on residential carbon inequality and its driving factors. Using the provincial panel data of Chinese residential sector from 2005 to 2017, this article examines residential CO2 emission inequality (carbon inequality) and its driving factors from a static and dynamic perspective to provide empirical support for the formulation of emission reduction policies and the allocation of regional emission reduction quotas. The results indicate that residential carbon inequality grew from 2005 to 2017. The energy intensity disparity was the main contributor in promoting carbon inequality increases, and its effects demonstrated an increasing trend. Per capita income disparity was the second largest factor in the growth of residential carbon inequality, with a contribution of approximately 20%. Additionally, the urban and rural structure also positively widened carbon inequality, but its contribution decreased. Conversely, the energy structure disparity actively facilitated a reduction of carbon inequality, although such active effects were minor. Based on these results, policies relevant narrowing the regional gaps of energy intensity, per capita income, and urbanization were proposed for decreasing carbon inequality of China's residential sector.

The greenhouse effect related to CO2 emissions has become the focus of extensive attention in China and elsewhere in the world. Thus, it is of great significance to identify the determinants of China's CO2 emissions (CCEs). Using an extended logarithmic mean Divisia index (LMDI), this study focused on investigating the roles of R&D (research and development) efficiency, R&D intensity, investment intensity, labor productivity, employment structure, urban employment ratio, and population urbanization level on CCEs. Then, the grey model and scenario analysis were used to forecast the future situation of CCEs. Results indicate that during 2000-2016, (1) R&D efficiency and energy intensity decline were the foremost contributors in emission mitigation. The rise of urban employment proportion in total employment was also helpful in reducing CO2 emissions. (2) R&D intensity contributed to emission-mitigation in most years, but restrained the decrease of CCEs in some specific years. (3) On the contrary, labor productivity growth, investment intensity increase, and urbanization level improvement all exerted positive impacts on CO2 emissions growth. (4) Based on the scenario analysis, the CCEs mitigation potential was predicted to be 723.15 and 1408.25 Mt in 2030 respectively under the moderate and aggressive scenarios. Finally, policy suggestions relevant to CCEs reduction were provided according to the empirical results.

The carbon emission rebound of the post-2008 financial crisis teaches us a lesson that avoiding a rebound in carbon intensity is key to prevent the carbon emission increase afterward. Although how carbon emission will change the world after the COVID-19 pandemic is unknown, it is urgent to learn from the past and avert or slow down the potential rebound effect. Therefore, this study aims to identify key drivers of carbon intensity changes of 55 sectors, applying the decomposition techniques and the world input-output data. Our results demonstrate that global carbon intensity fluctuates drastically when shocked by the global financial crisis, presenting an inversed-V shape for the period 2008–2011. Industrial carbon emission and gross output vary among different industries, the growth rate of industrial carbon intensity varies from -55.55% to 23.77%. The energy intensity effect and economic structure effect have opposite impacts on carbon intensity decrease, accelerating and hindering the decreasing carbon intensity, respectively. However, the energy mix effect has a minor impact on carbon intensity decrease. The industrial carbon intensity decomposition results show the impact of technological and structural factors are significantly different among industries. Moreover, the impact of energy intensity is slightly stronger than the energy mix. More measures targeting avoiding the rebound in carbon intensity should be developed.

Structural reforms are currently the key way to achieve emissions reduction targets. A comprehensive investigation of the relationship between structural changes and carbon emissions is essential for further policy formulation. This paper aimed to discuss the impact of structural changes on per capita carbon emissions from the four aspects of energy, trade, society and economy, while considering the effects of economic growth and energy intensity. The ordinary least squares, fully modified ordinary least squares regression analysis and Granger causality test were used to analyze the situation of 147 countries and four income groups from 1990 to 2015. The results showed that at the global level, economic growth and economic structure were respectively the most significant positive and negative factors affecting carbon emissions. There was the bidirectional granger causality relationship between global per capita carbon emissions and economic growth, and between global per capita carbon emissions and economic structure. At the income group level, consistent with the global results, economic growth and energy intensity increase promoted the increase in carbon emissions, whereas increasing renewable energy consumption helped to reduce carbon emissions. The influence direction and degree of the remaining three factors differed among different income groups. In addition, the causal relationships among the variables of the income groups except the low income group were relatively complicated. Finally, policy recommendations were offered to reduce per-capita carbon emission through adjusting economic, energy, social, and trade structure.

Environmental problems are immensely increasing and have a significant impact on eco-system. The growing concerns about the reserves of resources for future generation and strategies to mitigate environmental degradation have been the premise for decision-making. The present study estimated the impact of eco-efficiency and carbon footprints on the environment by incorporating the consumption and production patterns of resource utilization of 19 Asia-Pacific Countries by populations as it is directly proportional to energy intensity. Westerlund co-integration test and panel co-integration techniques to the data sets covering the time of 1990–2018 for different models are applied in the study. At a panel level, the findings show the relationship of eco-efficiency as the proportion of resource utilization with the urban population. Further, results depicted that the greater the consumption and production patterns, the greater will be the carbon footprints. The study opens up a new dimension for policymakers to design policies as a limited consumption per person for sustainable long-run efficiency.

The continued development of artificial intelligence (AI) has changed production methods but may also pose challenges related to energy consumption; in addition, the effectiveness of AI differs across industries. Thus, to develop efficient policies, it is necessary to discuss the effect of AI adoption on energy intensity and to identify industries that are more significantly affected. Using data on industrial robots installed in 16 Chinese industrial subsectors from 2006 to 2016, this paper investigates both the effect of AI on energy intensity and the channel through which this effect is transmitted. The empirical results show, first, that boosting applications of AI can significantly reduce energy intensity by both increasing output value and reducing energy consumption, especially for energy intensities at high quantiles. Second, compared with the impacts in capital-intensive sectors (e.g., basic metals, pharmaceuticals, and cosmetics), the negative impacts of AI on energy intensity in labor-intensive sectors (e.g., textiles and paper) and technology-intensive sectors (e.g., industrial machinery and transportation equipment) are more pronounced. Finally, the impact of AI on energy intensity is primarily achieved through its facilitation of technological progress; this accounts for 78.3% of the total effect. To reduce energy intensity, the Chinese government should effectively promote the development of AI and its use in industry, especially in labor-intensive and technology-intensive sectors.

In this paper, we study the recent energy use intensity (EUI) in kWh/m2/year of Hong Kong's secondary schools. We found a 22% increase of EUI from, 2003 (86 kWh/m2/year) to 2017 (105 kWh/m2/year) approximately. It may be due to the increase in the number of air-conditioned classrooms. We then use a novel two-stage regression-based approach to analyse the most important set of statistically significant energy performance factors. Then, the energy use intensity (EUI) is normalized with these energy performance factors to find the benchmarking scores used to assess whether the electricity tariff affects schools' energy efficiency. In the first stage of the approach, we first conduct three methods of fitting regression models, backward, forward, and stepwise (OLS), to find a set of normalized factors and construct the benchmarking model. The first stage provides the normalized EUI range, approximately between 50 and 163 kWh/m2/year. In the second stage, if better goodness of fit is desired in the second stage, convex nonparametric least squares (CNLS) regression analyses are employed with the same set of normalized factors. This two-stage regression-based approach is called the OLS-CNLS approach. Using simple EUI (kWh/m2/year) results, we find that high tariff schools' energy efficiency is better than low tariff schools. However, the benchmarking scores (normalized EUIs) of the OLS-CNLS approach show that the null hypothesis cannot be rejected. The energy efficiency of high tariff schools is not significantly better than that of low tariff schools. It implies that the tariff level is not a crucial factor associated with secondary schools' energy efficiency in Hong Kong. As the OLS-CNLS approach allows us to take all the significant energy performance factors into consideration to address the electricity tariff concern, the funding/budget policies probably motivate the school management to look for better EUI.

It is worthwhile to carry out carbon emission reduction of transportation, which is an important sector of energy consumption. Thus, we used the expanded structural decomposition analysis model and input–output analysis to investigate the structural emission reduction of transportation in China. The results reveal that within the research interval, (1) the energy intensity effect (EIE) was a major factor for reducing carbon emissions from China’s transportation sector, which is caused by the improvement of energy efficiency. However, the input structure effect (ISE) on emission reduction is not apparent. Therefore, the utilization efficiency of transportation in various industries has not been significantly optimized. (2) The final demand effect (FDE) is the main determinant of carbon emission growth in transportation sector due to the steady growth of demand for transportation across all industries. Simultaneously, secondary industries play a major role in the FDE, followed by tertiary and primary industries. However, the direct consumption coefficient of primary and tertiary industries to the transportation sector demand is lower than that of the secondary industries (e.g., heavy industry). Therefore, the dynamic optimization of industrial structure is conducive to reducing carbon emissions caused by FDE. (3) The energy structure effect (ESE) is shown to restrain the growth of carbon emissions with an increasing trend, caused by the increase and decrease in the proportion of low-carbon and high-carbon energy consumption structure, respectively, in the energy supply side of transportation sector. Diesel oil, gasoline, kerosene, and liquefied natural gas were the main contributors to carbon emission reduction in ESE, while raw and cleaned coal did not play a role in reducing carbon emissions in ESE. This study can provide practical guidance for China’s transportation sector to implement emission reduction more accurately based on the energy type and industry level.

The European Union pursues the European Green Deal strategy as well as Sustainable Development Goals, the main target of which is to become a carbon- neutral continent by 2050. Hence, a lot of environmental challenges need to be solved. Possible determinants in mitigating environmental challenges are financial inclusion, natural resources and interaction with a green environment. This concept implies preserving natural resources and a clean environment for future generations. However, there is still no clear evidence in the literature on how natural resources and financial inclusion interact with the green environment in the EU. Therefore, this paper aims at filling this gap. In order to obtain empirical results, the quantile regression econometric technique proposed by Koenker has been applied. The analyzed period was from 2004 to 2019 for EU-26 countries. The results show that higher energy intensity is the main cause of environmental degradation. However, financial inclusion in higher quantiles and natural resources rent lead to a reduction in carbon emissions. Our results also confirm that the EU has succeeded in decoupling economic growth from pollution. The robustness of the results was checked using a Powell’s quantile regression, which confirmed the relationship between a green environment and the variables analyzed. Thus, the results suggest that financial inclusion needs to be more integrated into energy and climate policies, especially in the early stages of development. In addition, large-scale green investments are needed in EU countries to further reduce energy intensity and create an effective green environment.

Industrial structure change can affect aggregate energy intensity through sector transfer effect. There are still obvious structural distortions in the process of China's industrial structure change. This structural distortion will inevitably have an adverse impact on the allocation efficiency of resources (including energy), which may hinder the decline of aggregate energy intensity. Based on the idea of ​​equalized labor productivity across sectors under the equilibrium of free entry and exit, this paper measures the industrial structure distortion index of China's provinces from 1978 to 2016, and examines the impact of the structural distortion index on China's energy intensity by using conventional panel data model estimators. The results show that during the period of 1978-2016, China's industrial structure distortion index decreased significantly, and the distortion index was a key determinant of the energy intensity at the province level. We also found that energy price and import and export were important factors driving the decline of China's energy intensity, and FDI had an obvious negative effect, while R&D expenditure had no significant impact on it. In order to reduce the energy intensity, China should dedicate to eliminating the micro-roots of the industrial structure distortion, establish a market-based factor price formation mechanism.

The data obtained as a result of dynamic tests of sand-lime brick samples were used to estimate its energy intensity. The experiments were carried out according the Kolsky method using a split Hopkinson bar. The graphs of the specific energy absorption of sand-lime brick were obtained at different deformation rates as a result of the numerical integration of dynamic deformation diagrams. In addition, dependences characterizing the growth of energy characteristics with an increase of the strain rate are plotted. Comparison of the studied properties of sand-lime brick with same properties of other brittle media is carried out.

Production companies are getting more and more aware of the relevancy of energy costs and the environmental impact of their manufactured products. Hence, the knowledge about the energy intensity of new process technologies as metal printing becomes increasingly crucial. Therefore, data about the energy intensity of entire process chains allow a detailed assessment of the life cycle costs and environmental impact of metal printed parts. As metal printing with Laser Powder Bed Fusion (LPBF) is applied from rapid prototyping to serial manufacturing processes more and more, sustainability data are useful to support a valid scale-up scenario and energetic improvements of the 3D-printing machinery as well as peripheral aggregates used in the process chain. The contribution aims to increase the transparency of the LPBF process chain in terms of its energy consumption. Therefore a generalized model to assess sustainability aspects of metal printed parts is derived. For this purpose, the LPBF process chain with the essential pre-, main- and post-processes is evaluated regarding its energy intensity. Here, the sub-processes, for example wet and dry cleaning of the printer, sieving of the metal powder or sand-blasting of the part are analyzed as well as the main printing process. Based on the derived experimental data from an installed, industry-like process chain, a model is created, which tends to generalize the experimental findings to evaluate other metal printed parts and process chain variants in terms of their energy intensity.

Community water supply is the local, consumer-level segment of water supply. For its ubiquity, an ability to predict the energy demand for community water supply would help better quantify the water-energy nexus at the community level. However, communities differ in size and distance from the water source, among other variations; no formalism is currently in use for making energy predictions. Here, we describe a conceptual model and mechanistic formulation for predicting the energy demand of a community water supply in the context of a regional, multi-community supply system. We make the novel methodological integration of scaling analysis and life cycle assessment to account for both network effects and life cycle effects on energy intensity. We also make the novel mechanistic law integration of hydraulic energy loss and allometric energy scaling to account for both linear transmission energy and nonlinear distribution energy. With these integrative steps, we create a novel “leaf on a stem” network model and mechanistically parameterized formula for predicting the energy demand of a community water supply, given in life cycle quantity and as the sum of community distribution energy in sublinear scaling with community size and regional transmission energy in linear scaling with the community’s distance from the regional water source. We validate the predictive model by empirically verifying the model parameters through a detailed case study of consumption communities within a regional supply system in the US Great Lakes region. This simple, mechanistic predictive model may help substantiate the concept of water-energy nexus and facilitate energy assessment for community water supply.

The present study explores the important contributors of energy efficiency in OECD countries, by taking the energy intensity and carbon intensity as a proxy of energy efficiency. Notably, we study the role of three indicators of export diversification (export product diversification, extensive margin and intensive margin) for energy intensity and carbon intensity in 29 OECD countries, which further allow us to draw new and innovative conclusions. In doing so, we use the data from 1990 to 2015 and employ alternative panel data estimations; sequential estimation, panel quantile regressions, system GMM and difference GMM, for an in-depth and detailed empirical analysis. In our empirical analysis, we robust check the impacts of export diversification on carbon intensity and investigate the role of institutional factors for energy intensity. The empirical outcomes provide robust evidence that all three indicators of export diversification help to reduce energy intensity and might be used as a policy factor in improving energy efficiency and sustainable environment. The empirical evidence further reports practical implications for policymakers and environmental scientists to achieve the greener growth, energy efficiency and sustainable development goals (SDG's) of OECD countries.

The paper analyzes the variation of carbon dioxide emissions produced by burning fossil fuels for energy production in the European Union member states between 1960 and 2018. It was analyzed the evolution of CO2 emissions produced by the combustion of coal, gas, and oil in 28 member states. The analysis showed that Cyprus, Portugal, Greece, and Spain recorded the highest increases in CO2 emissions in 2018 compared to 1960, while the Czech Republic and Latvia recorded the lowest increase in CO2 emissions by only 7% compared to 1960. Only four countries (Germany, Luxembourg, Sweden, and the United Kingdom), out of the 28 countries analyzed, had in 2018, CO2 emissions lower than or equal to those of 1960. In 2018, CO2 emissions generated by burning coal increased in eleven countries and decreased in the other seventeen countries, compared to 1960 emissions. CO2 emissions from gas combustion increased in all 28 countries analyzed, with the mention that in 1960, eleven countries had no gas consumption. Regarding the CO2 emissions generated by oil burning, they have increased in 26 countries and only two countries have registered emissions reduction below the value of 1960. EU’s energy intensity (ratio of gross energy consumption to gross domestic product) decreased by 37% between 1990 and 2017.

In order to quantify the contribution of the mitigation strategies, an extended Kaya identity has been proposed in this paper for decomposing the various factors that influence the CO2 emission. To this end, we provided a detailed decomposition of the carbon intensity and energy intensity, which enables the quantification of clean energy development and electrification. The logarithmic mean divisia index (LMDI) has been applied to the historical data to quantify the contributions of the various factors affecting the CO2 emissions. Further, the global energy interconnection (GEI) scenario has been introduced for providing a systematic solution to meet the 2°C goal of the Paris Agreement. By combining LMDI with the scenario analysis, the mitigation potential of the various factors for CO2 emission has been analyzed. Results from the historical data indicate that economic development and population growth contribute the most to the increase in CO2 emissions, whereas improvement in the power generation efficiency predominantly helps in emission reduction. A numerical analysis, performed for obtaining the projected future carbon emissions, suggests that clean energy development and electrification are the top two factors that can decrease CO2 emissions, thus showing their great potential for mitigation in the future. Moreover, the carbon capture and storage technology serves as an important supplementary mitigation method.

The aim of this study is to examine the factors affecting energy intensity and its components such as electricity intensity, oil intensity, gas intensity and coal intensity. We pay special attention to the roles of the fossil fuel subsidies, which include total fossil fuel, fossil-fuelled electricity, oil, gas and coal subsidies in developing countries. Using a generalized method of moments approach, it is shown that increase in fossil fuel, electricity, oil, gas and coal subsidies promote energy intensity or its component elements. The results further suggest that urbanization, industrialization and foreign direct investment also promote energy intensity. Conversely, it is also noted that real gross domestic product per capita and human capital development have negative impact on energy intensity and its components. Moreover, the countries are clustered according to their income level and the relationship in the variables are re-examined in each of the clusters. The results are not substantially different to those obtained for the total sample. The implications of these empirical findings are analysed in the paper.

Life Cycle Inventories must meet the data quality requirements set out by the goal and scope of a Life Cycle Assessment. The literature on the timing of updating inventory data to meet the data quality requirements is almost non-existent. In this study, data were taken from publicly available reports (corporate, peer-reviewed or governmental), congresses, proceedings, technical papers, international databases and LCAs. Monte Carlo simulations were used to test for variance; regression analysis was used to determine correlation between time and energy intensity. Different conventional automobile power train configurations were examined along with lightweight versions. Monte Carlo simulations were programmed for each automobile power train configuration. Least absolute residuals methods were used to confirm whether the variance seen in simulations was due to time or to other factors such as uncertainty or scattered data. The Monte Carlo simulations identified the variation by material and quantified their significance. Least absolute regression analysis identified and confirmed which materials have a clearly delineated temporal relationship: rubber, polyethylene terephthalate, polypropylene, lead, copper, steel, recycled steel, aluminum and recycled aluminum. Consequently, recommendations are given for the time intervals for necessary data updates which, depending on the material in question, are between two and five years.

This article aims to present historical rate of decoupling and based on that determine the scope for India to increase its mitigation beyond the NDC commitment. Empirical evidence on nature and rate of decoupling between energy related emission and economic growth for the period 1990–91 to 2012–13 in India is presented. In addition to estimating the magnitude of decoupling elasticity, decomposition analysis is also applied to understand how the four factors: activity growth, energy intensity change, structural change and fuel mix change, are driving the change in emission in India. Decoupling elasticity and Log Mean Divisia Index (LMDI) methods are used for decomposition. The results indicate presence of relative decoupling in India. The industrial sector leads among the four sectors -agriculture, industry, services and power generation in achieving this relative decoupling, mostly through improvement in energy efficiency and some structural changes. Results show that even in the business as usual scenario if India acts upon individual sector level mitigation potentials, it has the potential to raise mitigation ambition beyond current Nationally Determined Contributions (NDC) without adversely impacting economic growth. With continued high share of coal in the energy mix it is going to be difficult to achieve absolute decoupling.

The fuel cell reliability and durability are still the main factors limiting the large scale commercialization. To a certain degree, water content, transportation, distribution and state in the fuel cell influence the fuel cell State of Health (SOH). However, it's very difficult to measure water content inside fuel cell directly. The PEMFC system voltage fluctuate during hydrogen purging process, due to the removal of liquid water will affect the reactants transformation. Different internal water content will cause different voltage fluctuations. For this characteristic, The Energy Intensity of Reconstructed Vibrating Voltage (EIV) based on wavelet transformation is proposed and validated in this paper. The boundary value of EIV is determined to be 0.1 through experiments. The results show that the fuel cell voltage drop is reduced to 0.32 V/h from 1.39 V/h by using this method to avoid anode flooding. By several PEMFC system experiment results in test bench, this method can diagnosis the water content in PEMFC properly.

This paper empirically investigates the impact of economic growth and industrial structure on China's Nitrogen Oxides (NOx) emissions reduction in a comprehensive Kaya-PLS framework. It is found that the influencing coefficients of economic growth and industrial structure are 0.27 and 2.53, respectively. It indicates that economic growth and industrial structure do bring positive effects on China's NOx emissions, and industrial structure plays a much greater role than economic growth in the process of NOx emissions reduction in China. Moreover, we incorporate additional variables involving energy consumption structure, energy intensity, population and cement production to augment the NOx emissions function. In addition, six scenarios are set to calculate China's NOx emissions for 2019–2030. The prediction results show that China's NOx emissions decreased by 9.11% on average during 2019–2030 because of the decline of the proportion of secondary industry. It means that the decrease of proportion of secondary industry would make a significant decrease for China's NOx emissions. What's more, China's NOx emissions in six scenarios in 2020 are not more than 15.74 million tons, indicating that it is realistic to achieve China's reduction target on NOx emissions according to the 13th Five-Year Plan. Therefore, the government should adjust the energy structure and industrial structure as soon as possible to control China's NOx emissions and improve air quality.

The full life cycle of today's crystalline photovoltaic (PV) panel is dominated by a linear, open material flow paradigm. The Cradle-to-Cradle philosophy (C2C) applied in a Closed-Loop-Material-Cycle (CLMC) scenario seems promising to move towards a Circular Economy (CE). Environmental impacts associated with the End-of-life (EoL) phase of PV panels, particularly a CLMC scenario, have not yet been evaluated. To this end, this article uses the Life Cycle Assessment methodology to compare a linear Open-Loop-Material-System (OLMS) scenario with a novel CLMC system. Based on our results, the environmental impacts of a PV CLMC scenario are then compared with a Cadmium telluride (CdTe) panel CLMC scenario. In terms of environmental impacts, the recovery of PV materials in a CLMC scenario results in substantial improvements over an OLMS scenario. Closing the material flow has reduced the Climate Change impact factor (kg CO2 eq) by 74%, compared with the OLMS scenario. However, EoL PV recycling technology still remains behind in environmental and energy intensity terms when compared to the EoL CdTe panel recycling technology within a CLMC scenario. Furthermore, during the recycling processes, our results showed that the highest specific energy uptake was 3264 TJ for PV, while for CdTe it was 2748 TJ. On the other hand, the use of toxic chemicals to recover Si and Cd are shown to significantly contribute to the environmental impacts of both EoL PV and CdTe CLMC scenarios. Results show that the CLMC based on C2C principles has a favorable impact by reducing the environmental burden at the EoL. Nevertheless, it is imperative to reduce environmental burdens from the current thermochemical processes used to recycle silicon and to start considering the key role of C2C principles for PV panel design and recycling processes, aiming at the introduction of a CLMC system based on new standards and consistent regulations in order to reduce the environmental impacts of current PV panels, if a sustainable PV technology is desired.

Future electricity consumption may increase due to climate change, but the amplitude depends on the interaction between many uncertain mechanisms. Based on the linear model and policy model, the residential and commercial electricity consumption in Jiangsu province are projected under the shared socioeconomic pathways (SSPs). The linear model considers climate and socioeconomic factors, and the policy model also takes policy factors into account. We find that the cooling degree days (CDD) coefficient is about 3 times of heating degree days (HDD), which reflects that the cooling demand is much larger than heating, and also shows in the projection. The results of the policy model are generally lower than the linear model, which is the impact of policy factors. For example, the SSP1 and SSP2 of the policy model are 320 TW h and 241.6 TW h lower than the linear model in 2100, respectively. At the end of the 21st century, the residential and commercial electricity consumption in Jiangsu province will reach 107.7–937.9 TW h per year, 1.3–11.6 times of 2010. The SSP1 scenario under the policy model is based on feasible assumptions, and can be used as the target scenario for policymakers to establish energy intensity reduction targets.

The industrial sector has accounted for approximately 70% of China's total energy consumption over the past 30 years. Achieving green development is thus an important strategic goal of the Chinese industrial sector. The Chinese government has adopted various energy policies to facilitate industrial green development. Due to their different goals, these policies may have different impacts on the promotion of green development in the industrial sector. Using panel data on 31 provinces from 2007 to 2014, we examine the effects of three types of energy policies—renewable energy, energy conservation, and industrial upgrading—on industrial green development in China, measured using reduced energy intensity. Our empirical findings suggest that all three types facilitate industrial green development. However, effectiveness varies by policy type and region. Renewable energy and energy conservation policies have stronger impacts than policies aiming at upgrading traditional fossil fuel industries. Moreover, it takes longer for the effectiveness of industrial upgrading policies to be manifested, compared with the other two types. We discuss the implications of these empirical findings for future policymaking in promoting industrial green development.

Energy saving is essential to sustainable development and has been a major target set by the Chinese government. This study analyzes China’s energy intensity change during 1997–2015. This paper applies non-vertical adjusted structural decomposition analysis method to build comparable input-output tables and decomposes China’s energy use according to technical change, demand structure, import structure, import quantity, and export quantity for five types of energy. The results show that: (1) Both technical changes and demand structure changes are important driving forces for China’s energy intensity decrease; (2) Technical changes have played an increasingly important role in energy intensity decrease over time since 2002; (3) Technical change effect has caused the energy intensity of traditional high energy-consuming industries to decrease; (4) The results are robust under different degrees of non-vertical adjustments. Although technical change has played a critical role in China’s energy saving, the increase in demands will become increasingly important factors in changes to energy intensity and will require continued future research.

China must urgently accelerate its decrease of energy use, optimize its energy structure, reduce CO2 emissions, and promote the early realization of an ecological civilization. Simultaneously, meeting the growing consumer demand is one of the reasons for the increase in energy use. This study investigates the impacts of household consumption on energy use and CO2 emissions from the perspective of the lifestyle of Chinese residents. On the basis of the input–output model of 30 provinces, we analyze the current situation of energy use and CO2 emissions in different regions (spatial scale) with economic development and income improvement (time scale), investigate the pulling effect of household consumption in different provinces on industrial sectors, examine the influencing factors of indirect CO2 emissions from food, clothing, housing, and transportation in key regions, and explore the policy implications of the transition to a low-carbon lifestyle in different provinces. Results show that the fuel structure of Chinese residents should be optimized further. Total household energy consumption and total CO2 emissions considerably increased. In 2012, total household energy consumption accounted for nearly 30% of total energy consumption, while indirect CO2 emissions accounted for 66.3% of total household emissions. With regard to the structures of indirect household energy consumption, the housing sector accounted for the largest proportion, reaching 23.4% in indirect energy consumption in 2012. The pulling effect of the housing sector on industrial sectors was also evident. The decomposition analysis showed that the rapid increase in indirect household CO2 emissions was primarily due to the increase in per capita living expenditure. The consumption structures in different provinces produced various impacts, and the energy intensity effect was identified as an important factor for reducing indirect household CO2 emissions.

This empirical study examines the extent to which the introduction of NAFTA influenced the convergence behavior of CO2 emissions intensity and its determinants (energy intensity and carbonization index) associated with the countries of Canada, Mexico, and the U.S. Our results from tests of stochastic convergence, σ-convergence, and club convergence reveal convergence in both the pre- and post-NAFTA periods. This finding suggests the convergence behavior of CO2 emissions intensity and its determinants has not substantially changed with the introduction of NAFTA.

This empirical study is a new addition to the existing body of knowledge, which estimates the influence of industrialisation and urbanisation on carbon dioxide emissions in the APEC countries with a new panel estimation technique, the Dynamic Unrelated Seemingly Regression (DSUR). The data covering the years 1990 to 2014 have been used for analysis. Empirical findings of the current study reveal that industrialisation degrades the environment through releasing carbon dioxide to the air. Beside this, it is also observed that the energy intensity, urbanisation, and economic growth enhance the level of environmental pollution via CO2 emissions. Moreover, unidirectional causality is found between industrialisation and CO2 emission. For dynamic analysis, a time series analysis is also performed. Finally, policy recommendations regarding R & D are suggested for the industrial and energy sectors.

The article highlights the main trends in the development of world energy. The method of forecasting energy consumption is presented, taking into account the level and dynamics of energy consumption in general, as well as the use of individual energy resources with differentiation by countries and regions. In the present study, when forming the energy forecast, the key indicators determining the global energy demand are the dynamics of the energy intensity of GDP and per capita energy consumption. It is shown that in the total volume of world consumption of primary energy resources, oil will continue to occupy a leading position. Among fossil fuels, the demand for natural gas will increase most intensively. The share of coal in the structure of consumption of primary energy carriers will decrease, which is associated with the replacement of coal with natural gas in order to reduce carbon dioxide emissions. The greatest growth rates of demand among non-fossil fuels will be observed in renewable energy sources. The main growth in atomic energy consumption will be in the developing countries of the Asia-Pacific Region. Keywords: Energy consumption, Europe, Forecast, Renewable energy, Trends, World

During the last two decades CO2 emissions increased rapidly along with the growth of production and energy use in the industrial sector in Thailand. We use the Logarithmic Mean Divisia Index (LMDI) method to decompose the source of changes of CO2 emission level and CO2 emission intensity of the sector for the period 2005–2017. The results show that during this period, on average the amount of CO2 emissions and the CO2 emission intensity increased annually. The structural change effect helped reduce both the amount of CO2 emissions and the emission intensity. However, the increased energy intensity of individual industries led to an increase in the amount of CO2 emission and CO2 emission intensity. The study thus calls for policies to reduce energy intensity of firms in the manufacturing sectors in order for Thailand to benefit from industrial growth without having to incur greater polluting emissions. Keywords: CO2 emissions, LMDI decomposition, Manufacturing industries, Thailand

Prior consumer research has emphasized the importance of understanding the macro impact of consumer consumption on the environment. However, at this time, no tool exists to quantify the level and/or trajectory of that impact. Therefore, the purpose of this research is threefold: first, to introduce the STIRPAT model to the marketing industry; second, to begin its expansion; and third, to provide suggestions for future use of the STIRPAT in the field of marketing. The STIRPAT model is traditionally defined as the stochastic (ST) estimation of environmental impacts (I) by regression (R) on population (P), affluence (A) and technology (T). We expand the PAT aspect of the model to include additional elements of consumer behavior such as consumer spending, consumption of material goods, and energy intensity of those goods. Our results and suggestions for future expansion of the STIRPAT for marketing are then given.