Mehmet Pinar

The FEEM Sustainability Index: An Integrated Tool for Sustainability Assessment

The FEEM Sustainability Index (FEEM SI) proposes an integrated methodological approach to quantitatively assess sustainability performance across countries and over time. Three are the main features of this approach: (1) the index considers sustainability based on economic, environmental and social indicators simultaneously; (2) the framework used to compute the indicators, i.e. a Computable General Equilibrium (CGE) model, allows to generate projections on the future evolution of sustainability; and (3) the methodology used for the normalisation and aggregation of the indicators delivers a unique and comprehensive measure of sustainability. These features along with the multi-regional nature of the CGE model consent to perform policy evaluations and sustainability assessments for different countries or regions in the world. This chapter offers a methodological overview of the FEEM SI approach. To illustrate the potential of the methodology for the measurement of sustainability, the chapter also illustrates results from a climate policy scenario. In the mitigation scenario considered Annex I and Non-Annex I countries taking action towards climate change achieve the lower end of the pledges proposed at the 15th UNFCCC Conference of the Parties in Copenhagen. For countries putting into practice the policy, the environmental sphere more than offsets the related costs (economic pillar), leading to an overall improvement in sustainability. At world level, the outcome is positive even though carbon leakage in countries that are not acting reduces the effectiveness of the policy and the sustainability performance.

Measuring Human Development in the MENA Region

ABSTRACT We aim to assess welfare improvements in the Middle East and North Africa (MENA) region using the Human Development Index (HDI). We obtain weighting schemes that yield the best- and worst-case scenarios for measured human development, relying on consistent tests for stochastic dominance efficiency (SDE), with the official equally weighted HDI taken as a benchmark. In the best-case scenario index, life expectancy and GDP indexes receive the highest weights for the 1975–2005 period, while the education index is the dominant contributor to the worst-case scenario in the same period. In addition, we observe a relative change in the best- and worst-case scenarios between two fifteen-year periods. The GDP index is the main contributor to the best-case scenario between 1975 and 1990, whereas the education index is the main contributor to the worst-case scenario during that period. Life expectancy is the main contributor to the best-case scenario in the 1990–2005 period, while the GDP and education indexes are the primary contributors to the worst-case scenario during that period.

Quantile forecast combination using stochastic dominance

This paper derives optimal forecast combinations based on stochastic dominance efficiency (SDE) analysis with differential forecast weights for different quantiles of forecast error distribution. For the optimal forecast combination, SDE will minimize the cumulative density functions of the levels of loss at different quantiles of the forecast error distribution by combining different time-series model-based forecasts. Using two exchange rate series on weekly data for the Japanese yen/US dollar and US dollar/Great Britain pound, we find that the optimal forecast combinations with SDE weights perform better than different forecast selection and combination methods for the majority of the cases at different quantiles of the error distribution. However, there are also some very few cases where some other forecast selection and combination model performs equally well at some quantiles of the forecast error distribution. Different forecasting period and quadratic loss function are used to obtain optimal forecast combinations, and results are robust to these choices. The out-of-sample performance of the SDE forecast combinations is also better than that of the other forecast selection and combination models we considered.

Assessing Temporal Trends and Industry Contributions to Air and Water Pollution Using Stochastic Dominance

We employ a stochastic dominance (SD) approach to analyze the components that contribute to environmental degradation over time. The variables that are considered include countries’ greenhouse gas (GHG) emissions and water pollution. Our approach is based on pair-wise SD tests. First, we study the dynamic progress of each separate variable over time, from 1990 to 2005, within 5-year horizons. Then, pair-wise SD tests are used to study the major industry contributors to the overall GHG emissions and water pollution at any given time, to uncover the industry which contributes the most to total emissions and water pollution. We find that CO2 emissions not only contribute the most to the GHG emissions over time, but also increased within 15 year in the first-order SD sense. On the other hand, water pollution increased in a second-order SD sense. Pair-wise industry comparisons suggest that the major industry contributors to the CO2 emissions have always been the electricity and heat production sectors, while the transport sector has been the second contributor between 1990 and 2005. Finally, the food industry gradually became the major contributing industry for water pollution over time.

Forest Fires in Italy: An Econometric Analysis of Major Driving Factors

Despite the relevant fire risk to which Italy is subject from the north to the south, very few analyses focus on this topic. This article investigates the causes of forest fires frequency and intensity in Italy during the first decade of the twenty-first century. The dynamical aspects of fire danger are explored through the use of panel data techniques, which fully capture the impacts on forest fires regarding changes in both socioeconomic and climatic conditions. Italy is treated as a unique region in an initial model specification, and is then split into 3 geographical areas (north, center, and south) to capture locally specific aspects. Two different dependent variables are alternatively employed and a number of ad hoc tests are performed to corroborate the robustness of our estimates.The results highlight the importance of considering the fire situation separately for the northern, central, and southern parts of Italy. While the presence of railway networks positively affects fire risk, the impact of livestock depends on its specific composition. Favorable effects in fire reduction are represented by the increase in education levels (north and center) and touristic flows (north and south), and by the containment of illegal activities (south). Weather patterns appear to be important determinants throughout the Italian peninsula.

Institutions and information flows, and their effect on capital flows

We examine the empirical role of information flows and institutional quality in explaining the capital flows per capita across countries, and their role in explaining the so-called Lucas paradox -low levels of capital flows to poor countries. The findings of this paper suggest that countries with better institutions and high information flows receive high capital flows, and information flows also provides a partial explanation to the Lucas Paradox. The latter result is significant even after controlling for institutional quality, financial openness and human capital differences across countries, and using instrumental variable for information flows. This paper also examines the indirect effects of institutional quality on capital flows per capita through its impact on information flows and finds that countries with better institutional quality have higher levels of information flow. Accounting this indirect effect is economically important and papers that do not account for this indirect effect of institutions on capital flows per capita would underestimate the effect of institutions on capital flows per capita. Findings of this paper suggest that relatively poorer countries should improve their institutional quality and increase their access to worldwide information and promote investments in communications infrastructure to attract long-term capital flows.

Sensitivity of university rankings: implications of stochastic dominance efficiency analysis

ABSTRACT To create their rankings, university-ranking agencies usually combine multiple performance measures into a composite index. However, both rankings and index scores are sensitive to the weights assigned to performance measures. This paper uses a stochastic dominance efficiency methodology to obtain two extreme, case-weighting vectors using the Academic Ranking of Worldwide Universities (ARWU) and Times Higher Education (THE) data, both of which lead to the highest and lowest index outcomes for the majority of universities. We find that both composite scores and rankings are very sensitive to weight variations, especially for middle- and low-ranked universities.