Search results

The purpose of this paper is to review and evaluate energy use and performance, as well as existing energy efficiency policy and measures in the Latvian industrial sector, in order to highlight possible scenarios for energy efficiency improvements in the future.

The paper takes the form of a literature review and data analysis.

It is concluded that the existing energy efficiency measures for the industrial sector in Latvia do not work effectively. Also, it is expected that the planned measures for energy efficiency improvements for the industrial sector will not reach the defined energy saving targets in the framework of the Latvian NEEAP. Therefore, it is necessary to develop a common strategy on energy efficiency in the industrial sector in Latvia. This should be accomplished by reviewing the existing literature and data, as well as drawing up new, well‐designed energy efficiency measures.

The results of this study can be applied and used in the development of energy efficiency policy for the industrial sector in Latvia. The paper also points out the main weak points of existing policy measures that can be improved in order to meet the energy saving target for the Latvian industrial sector.

In this paper, the Latvian industrial sector's energy efficiency policy and related measures have been organized and evaluated for the first time. Also, the importance of energy efficiency improvements in the industrial sector has been highlighted. This is evident from the fact that, currently, almost no energy efficiency measure for the industrial sector in Latvia works sufficiently.

This study aims to identify sustainability indicators in the energy sector through a literature review and validate them from the perspective and context of professionals working in the sector in an emerging economy country, Brazil, considering the relationship of these indicators with the achievement of the targets set by the United Nations sustainable development goals (UN SDGs).

To accomplish this, a literature review on sustainability indicators specific to the energy sector was conducted. Subsequently, a research instrument (questionnaire) based on the identified indicators was developed and a survey was administered to professionals in the field. The collected data were analyzed using the Lawshe method.

The results revealed 20 indicators, distributed across environmental, economic and social dimensions. Among these, nine indicators were validated, including global impacts, local impacts, renewable energy production as a percentage of total production, greenhouse gas emissions, access to electricity, investment in the energy sector, installed capacity in the electricity sector, energy prices in the end-use sector and energy distribution and conversion efficiency.

Consequently, it was possible to determine which SDGs are directly impacted and provide a foundation for future actions that can contribute to the sustainable advancement of the energy sector in emerging countries.

Purpose – The paper addresses the MNCs’ sensitivity to corruption which varies across economic sectors depending on the interaction between sectoral characteristics and home-country formal institutions’ strength.

Design/methodology/approach – A theoretical framework is proposed based on the economic sector and host-country's institutional factors. The framework is empirically tested using 245 cross-border FDI valuations. Given that the energy sector is representative of high levels of industry concentration and government involvement – the sectoral characteristics considered to be moderating the relationship between corruption and FDI – the focus of the paper is on the energy sector. The study also tests the moderating effect of corruption distance.

Findings – The results indicate a lack of evidence that MNCs are deterred by corruption when investing in the energy sector of emerging and developing economies.

Research limitations/implications – The study provides a starting-point for further research of how economic sector characteristics can moderate the relationship between corruption and FDI. A key practical implication is that international anti-corruption measures are likely to be insufficient for some economic sectors.

Originality/value – The paper has proven to be of interest to the US State Department for studying the effectiveness of the international foreign bribery laws, such as the Foreign Corrupt Practices Act. The framework can assist in identifying economic sectors likely to be resistant to comply with the foreign bribery laws when conditions of weak host-country formal institutions are present. The study challenges and complements the prevailing theory that host-country corruption has a negative effect on inward FDI.

The purpose of this study is to dissect the transport infrastructure performance, public spending in transport infrastructure development and the manufacturing sector in determining the transport sector energy consumption.

An analysis of transport energy consumption with the transport infrastructure performance, public spending in transport infrastructure and manufacturing sector output in India using annual data for the period 1987–2019. The study used the autoregressive distributed lag (ARDL) bounds test approach along with FMOLS, DOLS and canonical cointegration regression (CCR) methods.

The results of the ARDL bounds test provide evidence for the long- and short-run relationships among study variables. It evidenced that transport infrastructure performance reduces transport energy consumption by using FMOLS, DOLS and CCR methods. Furthermore, the inference of the positive impact of value added in the manufacturing sector on transport energy consumption validates the higher energy demand of the manufacturing sector from a mobility perspective.

The estimated finding of this study is expected to be contributing to policy-making discussions on transport infrastructure and manufacturing sector development in an emerging economy like India with insights on energy consumption.

To the best of the authors’ knowledge, this is the first study that integrates the impact of manufacturing sector output on transport sector energy consumption along with transport infrastructure performance and public investment in the transport infrastructure.

The purpose of this paper is to determine priority order of Indian energy sector projects on investments and strategic dimension angles. Grey System Theory (GST) and COmplex PRroportional ASsessment (COPRAS-G) method, a flexible multi-criteria decision-making (MCDM) analyses, is used for this purpose to prioritize Indian energy sector projects, namely, coal, gas, hydro, solar and nuclear.

The GST-based MCDM approach of COPRAS is used. Five projects of energy sector are compared based on various grey criteria. These criteria were selected on the perspectives of life-cycle costing and management-thinking approach for prioritizing these projects. The GST-based COPRAS-G is described, and results are discussed to draw a strategic road map for measuring the sustainability in the energy sector.

On applying COPRAS-G on five energy projects, solar projects get high-priority order, and realistic scenario of results shows that renewable energy projects are preferred over the conventional projects such as coal and gas.

Here, COPRAS-G method is used as MCDM techniques. However, few other MCDM techniques such as fuzzy Preference Ranking Organization METHod for Enrichment Evolution, elimination and choice expressing reality and efficiency analysis technique with output satisficing can be also explored to outrank various Indian energy sector projects.

Indian energy sector involves high degree of complexity, and, therefore, it needs more flexibility to overcome the present barriers of effective decision-making. Grey decision theory-based method like COPRAS-G is able to address energy security dimensions on different scenario of energy supply, i.e. pessimistic, optimistic and realistic, precisely.

The results can provide guidance to the government or public sector regarding various possible investment options for energy supply and can help in drawing a rough trajectory of strategy toward energy security of the country.

This paper can supplement and act as the support for decision-making in conflicting situations specifically to have outlook of the sub-sector project on different flexible scenarios. Moreover, such work can synergize conflicting ideas of decision makers and various stakeholders of the Indian energy sector.

This paper aims to select the best scenario for energy demand forecast of residential and commercial sectors in Iran by using particle swarm optimization algorithm.

In this study, using variables affecting energy demand of residential and commercial sectors in Iran, the future status of energy demand in these sectors is predicted. Using the particle swarm optimization algorithm, both linear and exponential forms of energy demand equations were studied under 72 different scenarios with various variables. The data from 1968 to 2011 were applied for model development and the appropriate scenario choice.

An exponential model with inputs including total value added minus that of the oil sector, value of made buildings, total number of households and consumer energy price index is the most suitable model. Finally, energy demand of residential and commercial sectors is estimated up to the year 2032. Results show that the energy demand of the sectors will achieve a level of about 1,718 million barrels of oil equivalent per year by 2032.

To the best of our knowledge in this study a suitable model is selected for energy demand forecast of residential and commercial sectors by evaluation of various models with different variables as inputs.

The purpose of this paper is to reveal the dynamics of the Greek energy sector. As energy sectors contribute substantially to a national economy and stimulate national output and employment, it is important to identify their upward and downward linkages and interrelations with the other sectors of the economy.

To do this and capture such relations in the economy, a general equilibrium model is used. In specific, input–output (I–O) analysis is used and a model is specifically built for the Greek economy to examine in detail the energy sectors. Multiplier and linkage analysis is performed to assess their dynamics in terms of output, household income and employment.

Results indicate that the three energy sectors’ multipliers and elasticities, though are not ranking in the first places, are enough high indicating their strong linkages in the economy and their potentials to enhance the economy’s total output, employment and household income.

Further disaggregation of the economy’s energy sectors is needed to make clearer the separation among renewable and non-renewable sector, to identify and compare the dynamics and contribution of each category in the economy. Additionally, an environmental I–O model would indicate consequences on the environment and not just pure economic benefits.

Through the analysis, it can be seen that energy sectors and secondary energy products have the ability to drive a country’s economic activity through exports and intersectoral linkages, even if it is not a crude petroleum producing economy. Thus, knowledge of the economic impacts of such sectors is a valuable information.

The current study provides significant information of an economy’s energy sectors regarding their ability to support economic activity and employment. A general equilibrium model is used, examining the whole economy, to assess direct and indirect interrelationships.

The purpose of this paper is to predict the amount of energy consumption by using a suitable statistical method in some sectors and energy carriers, which has shown a significant correlation with greenhouse gas emissions.

After studying the correlation between energy consumption rates in different sectors of energy consumption and some energy carriers with greenhouse gas distribution (CO₂, SO₂, NO_X and SPM), the most effective factors on pollution emission will be first identified and then predicted for the next 20 years (2015 to 2004). Furthermore, to determine the appropriate method for forecasting, two approaches titled “trend analysis” and “double exponential smoothing” will be applied on data, collected from 1967 to 2014, and their capabilities in anticipating will be compared to each other contributing MSD, MAD, MAPE indices and also the actual and projected time series comparison. After predicting the energy consumption in the sectors and energy carriers, the growth rate of consumption in the next 20 years is also calculated.

Correlation study shows that four energy sectors (industry sector, agriculture, transportation and household-general-commercial) and two energy carriers (electricity and natural gas) have shown remarkable correlation with greenhouse gas emissions. To predict the energy consumption in mentioned sectors and carriers, it is proven that double exponential smoothing method is more capable in predicting. The study shows that among the demand sectors, the industry will account for the highest consumption rate. Electricity will experience the highest rate among the energy careers. In fact, producing this amount of electricity causes emissions of greenhouse gases.

Access to the data and categorized data was one of the main limitations.

By identifying the sectors and energy carriers that have the highest consumption growth rate in the next 20 years, it can be said that greenhouse gas emissions, which show remarkable correlation with these sectors and carriers, will also increase dramatically. So, their stricter control seems to be necessary. On the other hand, to control a particular greenhouse gas, it is possible to focus on the amount of energy consumed in the sectors and carriers that have a significant correlation with this pollutant. These results will lead to more targeted policies to reduce greenhouse gas emissions.

The tendency of communities toward industrialization along with population growth will doubtlessly lead to more consumption of fossil fuels. An immediate aftermath of burning fuels is greenhouse gas emission resulting in destructive effects on the environment and ecosystems. Identifying the factors affecting the pollutants resulted from consumption of fossil fuels is significant in controlling the emissions.

Such analyses help policymakers make more informed and targeted decisions to reduce greenhouse gas emissions and make safer and more appropriate policies and investment.