Strategic Environmental Assessment of Land Transportation: An Application of DEA with Undesirable Output Approach

The efficiency of land transportation contributes significantly to determining a country’s economic and environmental sustainability. The examination of land transportation efficiency encompasses performance and environmental efficiency to improve system performance and citizen satisfaction. Evaluating the efficiency of land transportation is a vital process to improve operation efficiency, decrease investment costs, save energy, reduce greenhouse gas emissions, and enhance environmental protection. There are many methods for measuring transportation efficiency, but few papers have used the input and output data to evaluate the ecological efficiency of land transportation. This research focuses on evaluating the environmental efficiency for land transportation by using the data envelopment analysis (DEA) method with undesirable output to handle unwanted data. By using this, the paper aims to measure the performance of land transportation in 25 Organization for Economic Co-operation and Development (OECD) countries in the period of 2015–2019, considered as 25 decision-making units (DMUs) in the model. For identifying the ranking of DMUs, four inputs (infrastructure investment and maintenance, length of transport routes, labor force, and energy consumption) are considered. At the same time, the outputs consist of freight transport and passenger transport as desirable outputs and carbon dioxide emission (CO2) as an undesirable output. The proposed model effectively determines the environment-efficient DMUs in a very time-efficient manner. Managerial implications of the study provide further insight into the investigated measures and offer recommendations for improving the environmental efficiency of land transportation in OECD countries.

The Impact of Technological Innovation, Research and Development, and Energy Intensity on Carbon Emissions: An Experience from BRICS and OECD Countries

The impact of technological innovation, research and development, and energy intensity on carbon dioxide emissions is examined in this study. A panel data econometric analysis of relevant variables extracted from the OECD and World Development Indicators databases for 36 OECD and 5 BRICS countries from 2005 to 2018 reveals that the Kao panel cointegration test revealed all countries, BRICS countries, and OECD countries exhibited cointegrated relationships regarding the selected variables. At this point, the correlation matrix shows that none of the independent variables has a strong correlation coefficient with the dependent variable. We also used two regression methods to evaluate the long-run association between the study's variables; the two-stage least square (2SLS) and panel generalized method of moments (GMM) both provide similar results, indicating that they are robust. According to the findings, technological innovation and R&D have a positive association with CO2 emissions, but energy intensity has a negative relationship with CO2 emissions.

The four global worlds of welfare capitalism: Institutional, neoliberal, populist and residual welfare state regimes

What welfare state regimes are observed when the analysis is extended globally, empirically and theoretically? We introduce a novel perspective into the ‘welfare state regimes analyzes’ – a perspective that brings developed and developing countries together and, as such, broadens the geographical, empirical and theoretical scope of the ‘welfare modelling business’. The expanding welfare regimes literature has suffered from several drawbacks: (i) it is radically slanted towards organisation for economic co-operation and development (OECD) countries, (ii) the literature on non-OECD countries does not use genuine welfare policy variables and (iii) social assistance and healthcare programmes are not utilized as components of welfare state effort and generosity. To overcome these limitations, we employ advanced data reduction methods, exploit an original dataset that we assembled from several international and domestic sources covering 52 emerging markets and OECD countries and present a welfare state regime structure as of the mid-2010s. Our analysis is based on genuine welfare policy variables that are theorized to capture welfare generosity and welfare efforts across five major policy domains: old-age pensions, sickness cash benefits, unemployment insurance, social assistance and healthcare. The sample of OECD countries and emerging market economies form four distinct welfare state regime clusters: institutional, neoliberal, populist and residual. We unveil the composition and performance of welfare state components in each welfare state regime family and develop politics-based working hypotheses about the formation of these regimes. Institutional welfare state regimes perform high in social security, healthcare and social assistance, while populist regimes perform moderately in social assistance and healthcare and moderate-to-high in social security. The neoliberal regime performs moderately in social assistance and healthcare, and it performs low in social security, and the residual regime performs low in all components. We then hypothesize that the relative political strengths of formal and informal working classes are key factors that shaped these welfare state regime typologies.

Digital and economic transformations for sustainable development promotion: A case of OECD countries

Digitalization, dematerialization of production and consumption, and structural shifts in the direction of service economy forming do promote to reduction of material use and sustainable development. The paper aims to investigate the role of digital, structural, economic, and social factors in sustainable development promotion in OECD countries. The paper uses the data on digital achievements, social and economic development of OECD member states from World Bank data sources for the period 2007–2018. The random-effects GLS regression model is used, and empirical regression models to estimate the influence of key factors related to digital transformation on GDP per capita and CO2 emissions per capita are constructed. The results of the regression analysis show that using the number of Internet users as an indicator for achievement in digitalization has a positive and statistically significant influence on GDP per capita due to lower transaction costs and higher share service economy. An increase in urbanization rates (as an indicator of capital concentrations and labor specialization) by one percent promotes a GDP per capita increase of 299 USD. Also, an increase in Gini coefficient by one percentage point correlates with decrease in GDP per capita on 196 USD and the reduction of CO2 per capita by 0.12 tones due to the structural shifts in aggregate demand. Still, improvements in digital transformations have no significant environmental effect in OECD members, while processes related to urbanization, income inequality, and share of industrial output are important drivers for CO2 per capita reduction. AcknowledgmentsThe paper contains the results of a study conducted within the framework of research projects: “Sustainable development and resource security: from disruptive technologies to digital transformation of Ukrainian economy” (No. 0121U100470); “Fundamental bases of the phase transition to an additive economy: from disruptive technologies to institutional sociologization of decisions” (No. 0121U109557).