► The MULTIMOORA method is extended with type-2 fuzzy sets. ► Generalized interval-valued trapezoidal fuzzy numbers are employed for MCDM. ► GITFNOWGA operator is used to aggregate expert ...assessments. ► A numerical example of personnel selection is provided.
Multi criteria decision making (MCDM) often involves uncertainty which can be tackled by employing the fuzzy set theory. Type-2 fuzzy sets offer certain additional means for the latter purpose. This paper therefore extends the MULTIMOORA method with type-2 fuzzy sets viz. generalized interval-valued trapezoidal fuzzy numbers. The proposed method thus provides the means for multi-criteria decision making related to uncertain assessments. Utilization of aggregation operators also enables to facilitate group multi-criteria decision making. A numerical example of personnel selection demonstrates the possibilities of application of the proposed method in the field of human resource management and performance management in general.
The identification of “industrial soot” or “vehicle exhaust” pollution facilitates developing proper measures for the mitigation of regional air pollution. In order to identify the pollution types at ...a regional level, this paper applies the Luenberger productivity indicator to decompose air pollutant emissions performance. Furthermore, we simultaneously consider pollution rates and the productivity change. Thus, we propose a new modeling framework allowing for the variable-specific decomposition of the environmental performance along time and quantity dimensions to identify the underlying patterns. The panel data for 30 provinces and autonomous regions are then applied to identify regional atmospheric pollution type. The results show that SO2 emission from industrial soot and NOx emissions from vehicle exhaust constitute an important source of regional atmospheric environmental inefficiency, though the former seems to be more decisive. The southeast coastal provinces showed generally lower levels of inefficiency, compared to the northwest inland area. During the period of the 11th Five-Year Plan of China, industrial SO2 emission performance contributed to the increase in the atmospheric environmental productivity, while traffic NOx emissions acted as a negative factor in this regard. Therefore, the government should seek to increase the intensity of environmental regulation in transportation sector. At the country level, technical progress associated with both types of pollutions was positive and thus exceed the negative efficiency change for the same variables. In particular, in Beijing-Tianjin-Hebei region, the productivity changes in industrial SO2 emissions and traffic NOx emissions indicate a “stably advancing” type. The results further indicate that there are 18 provinces of China which have experienced mixed-type pollution. Jilin and Hainan were classified as provinces experiencing vehicle exhaust gas pollution, whereas Guizhou was defined as that subject to industrial soot pollution. The government should formulate and implement diversified support and regulation policies to govern SO2 and NOx pollution at the regional level.
•It identifies regional level atmospheric pollution types for 30 Chinese provinces over the period from 2006 to 2015.•It applies the Luenberger productivity indicator to decompose atmospheric TFP to industrial SO2 and traffic NOx emissions.•It proposes a new modeling framework allowing for the variable-specific decomposition along time and quantity dimensions.•It shows that industrial SO2 and traffic NOx emissions constitute an important source of regional atmospheric inefficiency.•It suggests Chinese government should implement diversified regulations to govern SO2 and NOX pollution at regional levels.
•Two advanced optimization models were applied for EU energy policy scenarios development.•Several advanced MCDA were applied for energy policy scenarios ranking: WASPAS, ARAS, TOPSIS.•A Monte Carlo ...simulation was applied for sensitivity analysis of scenarios ranking.•New policy insights in terms of energy scenarios forecasting were provided based on research conducted.
Integrated Assessment Models (IAMs) are omnipresent in energy policy analysis. Even though IAMs can successfully handle uncertainty pertinent to energy planning problems, they render multiple variables as outputs of the modelling. Therefore, policy makers are faced with multiple energy development scenarios and goals. Specifically, technical, environmental, and economic aspects are represented by multiple criteria, which, in turn, are related to conflicting objectives. Preferences of decision makers need to be taken into account in order to facilitate effective energy planning. Multi-criteria decision making (MCDM) tools are relevant in aggregating diverse information and thus comparing alternative energy planning options. The paper aims at ranking European Union (EU) energy development scenarios based on several IAMs with respect to multiple criteria. By doing so, we account for uncertainty surrounding policy priorities outside the IAM. In order to follow a sustainable approach, the ranking of policy options is based on EU energy policy priorities: energy efficiency improvements, increased use of renewables, reduction in and low mitigations costs of GHG emission. The ranking of scenarios is based on the estimates rendered by the two advanced IAMs relying on different approaches, namely TIAM and WITCH. The data are fed into the three MCDM techniques: the method of weighted aggregated sum/product assessment (WASPAS), the Additive Ratio Assessment (ARAS) method, and technique for order preference by similarity to ideal solution (TOPSIS). As MCDM techniques allow assigning different importance to objectives, a sensitivity analysis is carried out to check the impact of perturbations in weights upon the final ranking. The rankings provided for the scenarios by different MCDM techniques diverge, first of all, due to the underlying assumptions of IAMs. Results of the analysis provide valuable insights in integrated application of both IAMs and MCDM models for developing energy policy scenarios and decision making in energy sector.
•Energy & environmental inefficiency of the 45 cities in China is analyzed.•Variable-specific productivity growth measures are used.•Bounded-adjusted Measure and Luenberger Productivity Indicator are ...applied.•Dagum Gini coefficient is employed to measure the regional disparities of energy use.•Policy guidelines are presented based on the productivity change directions.
Analyzing green transformation of energy use and pollutant emissions in China’s “Three Regions and Ten Urban Agglomerations” (TRTAs) allows effectively promoting sustainable development in the country. This paper applies Data Envelopment Analysis, namely the Bounded-adjusted Measure (BAM) relying on the additive structure, to measure the technical inefficiency and productivity change across TRTAs in China. The technical inefficiency and productivity change observed for TRTAs are 0.29 and 2.29% respectively. The decomposition results indicate that industrial energy consumption, industrial sulfur dioxide (SO2), and industrial soot & dust emissions are the main variables causing TRTAs’ industrial operation inefficiency. Spatially, environmental inefficiency of the above-mentioned three variables in North China Urban Agglomeration (NA) is higher than those in Northwest, Yangtze and Pearl River Delta Urban Agglomerations (NWA, YRDA, and PRDA respectively). Decomposition of the Gini coefficient shows that the performance associated with the three variables varies among the regions with intra-regional differentiation of the PRDA being the highest. Given the regional patterns in productivity change, more efforts should be directed towards improving technical progress on industrial energy conservation in NWA. Furthermore, regulations on industrial air pollutant emissions, joint mitigation and monitoring of the key indicators in PRDA should also be emphasized.
The agricultural sustainability issues are widely addressed in scientific literature and various reports by international organizations. However, there is lack of harmonized approach in addressing ...agricultural sustainability issues as different policies are targeting different sustainability issues in agriculture. This article analyses sustainable agriculture development and agriculture sustainability concepts and sustainability assessment approaches and tools developed for agriculture sector. Based on systematic critical literature review, this article develops the new indicators framework for sustainability assessment in agriculture which allows us to achieve harmonization of sustainable development, climate and agricultural policies in European Union (EU). The proposed indicators framework allows us to address the main sustainability issues of agriculture by linking them with sustainable development goals, environmental, climate and rural development policy priorities in EU. The main contribution of this article is linking rural policy goals with sustainable development, climate change mitigation and environmental policy goals by providing agricultural sustainability assessment framework allowing us to track these linkages through indicators system.
Bioeconomy is an important element of European Union (EU) political agenda. Promotion of bioenergy is one of the main aspects of bioeconomy strategy. The aim of this paper is to show how the ...development of bioenergy can contribute to climate change (and the associated policy). Specifically, we look into the possible reduction of GHG emissions within the framework of Environmental Kuznets Curve (EKC). The panel models are estimated for the EU countries by modifying the classical EKC by including in the EKC model biomass and other renewables. The results showed that the coefficient associated with GDP decreases when renewables are included in the model. More specifically, the more types of renewables are included, the lower values of the coefficient associated with the linear term are observed. Furthermore, the effect of biomass on the reduction of GHG emission is higher if opposed to that caused by the other renewable resources. If we hold other factors fixed, increase in biomass use of 1% would reduce GHG emission by 0.089%, whereas the effect of the other renewable energy sources is 0.025%. Therefore, the development of bioeconomy and the promotion of bioenergy are one of the main tools for climate change mitigation.
This paper analyses the trends in energy security across the three Baltic States, namely Estonia, Latvia and Lithuania. The period of 2008–2012 is covered in the analysis. The aggregate measures of ...energy security are devised by the means of multi-criteria decision making techniques. The choice of indicators of energy security is based on the priorities set out in the European Union energy policy. The proposed system relies on the objective weighting that requires no expert assessment. However, this approach is also supplemented by the restricted models, where certain bounds are defined for groups of criteria, describing energy security in economic, energy supply chain, and environmental dimensions. The results show that Latvia maintained the highest level of energy security irrespectively of the multi-criteria approach taken.
With the increasing affluence, the differences in CO2 emission between urban and rural residential sectors are remarkable and show an increasing trend. In case of China, residential sector accounts ...for a substantial share of the national CO2 emission, bringing greater pressure to achieve the goal of carbon peak. Analyzing the emission inequality trend and its drivers is essential for formulating effective CO2 emission reduction policies. However, the existing literature lacks relevant analysis from the viewpoint of urban-rural disparity. Hence, this study decomposes the CO2 emission inequality of China's urban and rural residential consumption into four factors by combining the Theil index and Kaya decomposition. The results suggest that, in 2005–2020, the per capita CO2 emission of rural residential consumption increased to a higher extent than those of urban households, with large differences in spatial distribution. Decomposition of the per capita CO2 emission inequality for residential sector shows that the primary source is the inequality within the groups, mainly from the urban intra-group inequality. Based on the static decomposition, energy intensity appears as the main factor of urban–rural inequality. The dynamic decomposition shows that there have been differences in the factors of the change in the Theil index between urban and rural areas across sub-periods.
•CO2 emission inequality is discussed based on urban-rural differences.•A Kaya-Theil decomposition is developed.•The case of China is considered via static and dynamic decomposition.•The evolution and drivers of CO2 emission inequality are explored.
Even though Lithuania has seen extensive social and political transformations, there is still a gap in the literature on environmental performance of Lithuanian economy. Specifically, the ...environmental pressures stemming from economic activities have never been analysed in lines with the principles of production economics. This paper, therefore, aims to estimate the environmental performance index (EPI) for Lithuanian economic sectors. The paper presents the underlying trends in greenhouse gas emissions in Lithuania. The environmental performance index is estimated by employing the data envelopment analysis in the spirit of the Hicks-Moorsteen indices. The present analysis is based on the data from the World Input-Output Database. Specifically, the environmental technology is defined in terms of the value added, carbon and nitrous oxide emissions, labour, capital and energy. Results of the analysis imply that the analysed economic sectors can be grouped into the four clusters with respect to the mean EPI and its rate of growth. Pulp, paper and agricultural sectors fall into the best-performing group, featuring the highest mean EPI and the highest growth rate. Petroleum production and air transport sectors are those featuring the lowest environmental performance. Therefore, the latter sectors should be given an especial importance when developing strategies for increase in sustainability.
•The paper estimates the environmental performance index for Lithuanian economic sectors.•Data envelopment analysis is employed in the spirit of the Hicks-Moorsteen indices.•The analysed economic sectors are grouped into the four clusters.•Pulp, paper, and agricultural sectors fell into the best-performing group.•Petroleum and air transport sectors appeared in the worst-performing group.
This paper employs the slack-based measure method and an extended Luenberger productivity indicator to estimate and decompose the atmospheric environmental performance under the constraints of energy ...and atmospheric pollutant emissions i.e., the growth of the atmospheric environment total factor productivity (AETFP) of the “three regions and ten urban agglomerations” (TRTAs) in China. Specifically, undesirable output is considered as both carbon and air pollutant emissions, i.e., CO
2
, SO
2
, and NO
x
emissions. Also, based on the proposed approach, we identify the different paths of the technical change as a crucial driver of the AETFP growth. Furthermore, using the spatial econometric model with a symmetric geographical distance weight matrix and an asymmetric economic geography weight matrix, we investigate the effect of different types of environmental regulation on the AETFP growth to verify the Porter hypothesis in China. The results show that the main drivers of China’s atmospheric environment inefficiency are air pollutant emissions (SO
2
and NO
x
), carbon emissions, and fossil energy use. Spatially, the environment inefficiency presents a decreasing trend from northern China to southern China. The improved performance of SO
2
emissions made more contributions to the AETFP growth during China’s 11th “Five-Year Plan” period (2006–2010), while NO
x
emissions has a marginal positive effect on the AETFP growth is marginal. Despite the differences in the technical change across regions, the technical progress offsets the negative impact of declining technical efficiency on the AETFP growth. Overall, energy-saving and emission-reduction policies and technologies in TRTAs exert a decisive influence on the AETFP growth. In particular, the spatial econometric results indicate that the market-motivated environmental regulation has a positive effect on the AETFP growth and thus conforms to the Porter hypothesis in China but does not cause the “race-to-the-bottom” effect among local governments, while the command-and-control oriented regulation leads to a “race-to-the-bottom” effect and undermines the AETFP growth.
