Using Data Envelopment Analysis to Assess the Relative Efficiency of Different Climate Policy Portfolios

Within the political, scientific and economic debate on climate change, the process of evaluating climate policies ex-ante, during and/or ex-post their lifetime, is receiving increasing attention from international institutions and organisations. The task becomes particularly challenging when the aim is to evaluate strategies or policies from a sustainability perspective. The three pillars of sustainability should then be jointly considered in the evaluation process, thus enabling a comparison of the social, the environmental and the economic dimensions of the policy’s impact. This is commonly done in a qualitative manner and is often based on subjective procedures. The present paper discusses a data-based, quantitative methodology to assess the relative performances of different climate policies, when long term economic, social and environmental impacts of the policy are considered. The methodology computes competitive advantages as well as relative efficiencies of climate policies and is here presented through an application to a sample of eleven global climate policies, considered as plausible for the near future. The proposed procedure is based on Data Envelopment Analysis (DEA), a technique commonly employed in evaluating the relative efficiency of a set of decision making units. We consider here two possible applications of DEA. In the first, DEA is applied coupled with Cost-Benefit Analysis (CBA) in order to evaluate the comparative advantages of policies when accounting for social and environmental impacts, as well as net economic benefits. In the second, DEA is applied to compute a relative efficiency score, which accounts for environmental and social benefits and costs interpreted as outputs and inputs. Although the choice of the model used to simulate future economic and environmental implications of each policy (in the present paper we use the FEEM RICE model), as well as the choice of indicators for costs and benefits, represent both arbitrary decisions, the methodology presented is shown to represent a practical tool to be flexibly adopted by decision makers in the phase of policy design.Climate, Policy, Valuation, Data envelopment analysis, Sustainability

Green manufacturing and environmental productivity growth

Purpose: Due to stringent regulations on carbon emissions, green manufacturing has become a critical issue in manufacturers’ strategic planning. Manufacturers are greening production through carbon abatement activities. This study aims to investigate the factors that influence the effects of carbon abatement on environmental productivity growth. Design/methodology/approach Using data envelopment analysis with directional distance function, this study examines productivity growth associated with carbon abatement under regulated and unregulated production technologies. A pollution abatement index is constructed for determining the effects of carbon abatement on environmental productivity growth. Panel data of eighteen European countries in paper and pulp and coke sectors are collected for the analysis. Findings The empirical findings reveal that carbon abatement may positively or negatively affect environmental productivity growth which is dependent on the nature of technology in a sector, the innovation capabilities of a country and environmental regulations. Originality/value Conventional approaches in measuring productivity changes do not normally take undesired outputs (e.g. carbon emissions) into consideration. This study contributes to literature by constructing a pollution abatement index that considers productivity changes under a joint production technology (where both desired and undesired outputs are considered). The findings enhance current understandings on the effectiveness of carbon abatement activities and help managers establish corporate environmental strategies to adopt green manufacturing

Data envelopment analysis: uncertainty, undesirable outputs and an application to world cement industry

Starting from the pioneering papers by Charnes, Cooper and Rhodes (CCR model) and Banker, Charnes and Cooper (BCC model), a large number of papers concerning Data Envelopment Analysis (DEA) with outputs uncertainty appeared in the literature. In particular, chance-constrained programming is the most used technique to include noise variations in data and to solve data envelopment analysis problems with uncertainty in data. Chance-constrained programming admits random data variations and permits constraint violations up to specified probability limits, allowing linear deterministic equivalent formulations in case a normal distribution of the data uncertainty is assumed. The standard DEA models rely on the assumption that inputs are minimized and outputs are maximized. However, both desirable and undesirable (e.g., pollutants or wastes) output factors may be present. The undesirable and desirable outputs should be treated differently when we evaluate the production performance: if inefficiency exists in the production, the undesirable pollutants should be reduced to improve efficiency. In order to include undesirable factors in DEA models, according to the literature, two different approaches can be used to model undesirable factors: one group of DEA models treats them as inputs, whereas a second group considers them as undesirable outputs. DEA models with undesirable factors are particularly suitable for models where several production inputs and desirable and undesirable outputs are taken into account, in order to provide an eco-efficiency measure. In this Ph.D thesis alternative DEA models, which consider both uncertain and undesirable outputs, are proposed and studied. In particular, in the first part of this thesis two different models with uncertain outputs and deterministic inputs are proposed with the aim to move away the classical chance-constrained method and to obtain a more accurate DMU ranking whatever situation occurs. Specifically speaking, the proposed models remove the hypothesis of normal data distribution and use a scenario generation approach to include data perturbations. For the sake of completeness, these models are compared with two further ones based on an expected value approach, where uncertainty is managed by means of the expected values of random factors both in the objective function and in the constraints. Deeply speaking, the main difference between the two proposed models and the expected value approaches lies in their mathematical formulation. In the new models, based on the scenario generation approach, the constraints concerning efficiency level are expressed for each scenario. On the other hand, in the expected value models the constraints are satisfied in expected value. As a consequence, the models proposed in the thesis result to be more selective in finding a ranking of efficiency, thus becoming useful strategic management tools aimed to determine a restrictive efficiency score ranking. In the second part of this study, we focus on environmental policy and eco-efficiency. Nowadays, one of the most intensively discussed concepts in the international political debate is, in fact, the concept of sustainability and the need for eco-efficient solutions that enable the production of goods and services with less energy and resources and with less waste and emissions (eco-efficiency). In particular, we consider the environmental impact of CO2 in cement and clinker production processes. Cement industry is, in fact, responsible for approximately 5% of the current worldwide CO2 emissions. DEA models can provide an appropriate methodological approach for developing eco-efficiency indicators. A cross-country comparison of the eco-efficiency level of the worldwide cement industry is presented by applying both a data envelopment analysis and a directional distance function approach. These tools result to be particularly suitable for models where several production inputs and desirable and undesirable outputs are taken into account. Strong and weak disposability assumptions are analyzed in order to evaluate the impact of environmental regulations interpreted as the cost of regulation. The few papers appeared in the literature of eco-efficiency in cement production analyze the emission performance trends only from an interstate point of view. In this thesis a worldwide study has been carried on, covering 90% of the world's cement production by means of 21 countries, European (EU) and non-European (non-EU) ones. The obtained results show that the efficiency level mainly depends on decisions to invest in alternative raw materials and alternative fuels, both in the case of regulated countries and in the case of voluntary emission-trading schemes. This study highlights, both at national and international levels, the possibility of reducing CO2 emissions and expanding cement production. The use of alternative raw materials, alternative fuels and the possibility of producing blended cements, which require less energy consumption and reduce pollutant emissions, seem to be appropriate means. Environmental regulations can provide incentives in terms of tax exemption benefits or more restrictive pollutant limits. Finally, we try to answer to the following questions: do undesirable factors modify the efficiency levels of cement industry? Is it reasonable to omit CO2 emissions in evaluating the performances of the cement sector in different countries? In order to answer to these questions, alternative formulations of standard data envelopment analysis model and directional distance function are compared both in presence and in absence of undesirable factors. This analysis shows that the presence of undesirable factors greatly affects efficiency levels. Efficiency levels are influenced by investments in best available technologies and by the utilization of alternative fuels and raw materials in cement and clinker production processes. The original results of this Ph.D. thesis have been collected in the following research papers: • Riccardi R. and R. Toninelli. Data Envelopment Analysis with outputs uncertainty. Journal of Information & Optimization Sciences, to appear. • Riccardi R., Oggioni G. and R. Toninelli. The cement industry: eco-efficiency country comparison using Data Envelopment Analysis. Journal of Statistics & Management Systems, accepted for publication. • Riccardi R., Oggioni G. and R. Toninelli. Eco-efficiency of the world cement industry: A Data Envelopment Analysis. Energy Policy, Vol. 39, Issue 5, p. 2842-2854, 2011, available online at: http://dx.doi.org/10.1016/j.enpol.2011.02.057 • Riccardi R., Oggioni G. and R. Toninelli. Evaluating the efficiency of the cement sector in presence of undesirable output: a world based Data Envelopment Analysis. Technical Report n. 344, Department of Statistics and Applied Mathematics, University of Pisa, 2011, submitted to Resource and Energy Economics. The research topic considered in this thesis shows many different lines for future developments. In particular, from a theoretical point of view, starting from the models proposed in Riccardi and Toninelli (2011), we are studying for a bi-objective like DEA formulation where both uncertainty desirable and undesirable factor are taken into account. As regards the applicative aspects, we are also studying and applying bootstrap techniques to manage uncertainty and generate empirical distributions of efficiency scores, in order to capture and analyze the sensitivity of samples with respect to changes in the estimated frontier

A Two-Stage DEA Model to Evaluate the Technical Eco-Efficiency Indicator in the EU Countries

This paper evaluates the evolution of eco-efficiency for the 27 European Union (EU) countries over the period 2008–2018, provided the traditional high concerns of the EU concerning the economic growth-environmental performance relationship. The EU has triggered several initiatives and regulations regarding environmental protection over the years, but as well the Sustainable Development Goals demand it. Under this setting, we conduct a two-stage analysis, which computes eco-efficiency scores in the first stage for each of the pairs EU 27-year, through the nonparametric method data envelopment analysis (DEA), considering the ratio GDP per capita and greenhouse gas emissions (GHG). In the second stage, scores are used as a dependent variable in the proposed fractional regression model (FRM), whose determinants considered were eight pollutants (three greenhouse gases and five atmospheric pollutants). CO2/area and N2O/area effects are negative and significant, improving the eco-efficiency of the EU 27 countries. When the efficient European countries are excluded from the estimations, the results evidence that CO2/area and CH4/area decrease the DEA score. The country with the lowest GHG emissions and pollutant gases was Ireland, being the country within the considered period that mostly reduced emissions, particularly SOx and PM10, increasing its score.info:eu-repo/semantics/publishedVersio

A framework for measuring global Malmquist–Luenberger productivity index with CO2 emissions on Chinese manufacturing industries

China has achieved significant progress in terms of economic and social developments since implementation of reform and open policy in 1978. However, the rapid speed of economic growth in China has also resulted in high energy consumption and serious environmental problems, which hindering the sustainability of China's economic growth. This paper provides a framework for measuring eco-efficiency with CO2 emissions in Chinese manufacturing industries. We introduce a global Malmquist-Luenberger productivity index (GMLPI) that can handle undesirable factors within Data Envelopment Analysis (DEA). This study suggested after regulations imposed by the Chinese government, in the last stage of the analysis, i.e. during 2011–2012, the contemporaneous frontier shifts towards the global technology frontier in the direction of more desirable outputs and less undesirable outputs, i.e. producing less CO2 emissions, but the GMLPI drops slightly. This is an indication that the Chinese government needs to implement more policy regulations in order to maintain productivity index while reducing CO2 emissions

Energy use and related emissions of the UK residential sector: quantitative modelling and policy implications

Reducing energy demand and carbon emissions from the UK housing stock through efficiency improvements is the focus of policy interest. The 2008 UK Climate Change Act set legally binding targets of an 80% reduction in greenhouse gas emissions against a 1990 baseline. The majority of emissions in the residential sector are carbon dioxide emissions arising from energy used for heating homes and water, cooking, lighting and electrical appliances. The sector s contribution to total UK emissions is significant and therefore reducing energy use in homes is an important factor if the UK is to meet its targets. In this research an initial survey of studies of the residential sector has been conducted to review factors considered to influence energy use and related emissions in UK housing. Further review identified energy and climate change policy instruments and structural change in the energy supply sector between 1970 and the present. A subsequent time-line of policy and events describes the changing, historical policy landscape related to energy efficiency improvements in the sector. As a result of these reviews, a need to better understand how householders have responded to technical energy efficiency improvements in housing, and the influence of social and economic factors, was identified as a research gap. In order to model householders historical behaviour Data Envelopment Analysis (DEA) was identified as an innovative approach for this field of research as a potential means to measure sector efficiency in a new way. The analysis has two stages. In the first, DEA is used to measure the relative efficiency with which the UK housing sector has managed its energy use and related emissions to deliver energy services such as space heating and lighting to householders. In the second stage, multiple regressions are used to examine whether the variability over time in the efficiency measure can be explained by policy interventions, energy market developments, and economic and social factors. DEA is a method for modelling the relative performance efficiency with which an observed sample converts measurable inputs to quantitative outputs. In this research, samples consist of annual observations of the UK housing stock, using data largely taken from DECC s UK housing energy fact file. An efficiency frontier of performance enveloping the observed sample points as closely as possible is constructed through DEA mathematical programming. The core of the analysis lies in identifying relevant quantitative input and output measures from available data. A range of measures of comfort and energy service levels to represent energy service outputs, and household energy and emissions data to represent inputs are examined in the analysis. The result is a timeline of efficiency performance that can be related to socio-economic change and the history of policy interventions. The analysis shows that the efficiency of the UK housing stock to manage its energy use and related emissions has not followed the steady upward trend that might have been expected from technical innovation. There is evidence of rebound effects over time, with householders behaviour in response to technical efficiency improvements acting to raise comfort levels rather than lower energy usage. Nevertheless, statistically significant roles can be identified for factors such as income, price and tenure which have implications for policy design and control and lead to a number of policy recommendations

Accounting for Uncertainty Affecting Technical Change in an Economic-Climate Model

The key role of technological change in the decline of energy and carbon intensities of aggregate economic activities is widely recognized. This has focused attention on the issue of developing endogenous models for the evolution of technological change. With a few exceptions this is done using a deterministic framework, even though technological change is a dynamic process which is uncertain by nature. Indeed, the two main vectors through which technological change may be conceptualized, learning through R&D investments and learning-by-doing, both evolve and cumulate in a stochastic manner. How misleading are climate strategies designed without accounting for such uncertainty? The main idea underlying the present piece of research is to assess and discuss the effect of endogenizing this uncertainty on optimal R&D investment trajectories and carbon emission abatement strategies. In order to do so, we use an implicit stochastic programming version of the FEEM-RICE model, first described in Bosetti, Carraro and Galeotti, (2005). The comparative advantage of taking a stochastic programming approach is estimated using as benchmarks the expected-value approach and the worst-case scenario approach. It appears that, accounting for uncertainty and irreversibility would affect both the optimal level of investment in R&D –which should be higher– and emission reductions –which should be contained in the early periods. Indeed, waiting and investing in R&D appears to be the most cost-effective hedging strategy.Stochastic Programming, Uncertainty and Learning, Endogenous Technical Change

Payments for environmental services : incentives through carbon sequestration compensation for cocoa-based agroforestry systems in Central Sulawesi, Indonesia

Up to 25 percent of all anthropogenic greenhouse gas emissions are caused by deforestation, and Indonesia is the third largest greenhouse gas emitter worldwide due to land use change and deforestation. On the island of Sulawesi in the vicinity of the Lore Lindu National Park (LLNP), many smallholders contribute to conversion processes at the forest margin as a result of their agricultural practices. Specifically the area dedicated to cocoa plantations has increased from zero (1979) to nearly 18,000 hectares (2001). Some of these plots have been established inside the 220,000 hectares of the LLNP. An intensification process is observed with a consequent reduction of the shade tree density. This study assesses which impact carbon sequestration payments for forest management systems have on the prevailing land use systems. Additionally, the level of incentives is determined which motivates farmers to desist from further deforestation and land use intensification activities. Household behaviour and resource allocation is analysed with a comparative static linear programming model. As these models prove to be a reliable tool for policy analysis, the output can indicate the adjustments in resource allocation and land use shifts when introducing compensation payments. The data was collected in a household survey in six villages around the LLNP. Four household categories are identified according to their dominant agroforestry systems. These range from low intensity management with a high degree of shading to highly intensified shade free systems. At the plot level, the payments from carbon sequestration are the highest for the full shade cocoa agroforestry system, but with low carbon prices of € 5 tCO2e-1 these constitute 5 percent of the cocoa gross margin. Focusing on the household level, however, an increase of up to 18 percent of the total gross margin can be realised. Furthermore, for differentiated carbon prices up to € 32 tCO2e-1 the majority of the households have an incentive to adopt the more sustainable shade intensive agroforestry system. A win-win situation seems to appear, whereby, when targeting only the shade intensive agroforestry systems with carbon payments, the poorest households economically benefit the most and land use systems with high environmental benefits are promoted.payments for environmental services, carbon sequestration, agroforestry systems, cocoa, linear programming, economic incentives, poverty, Environmental Economics and Policy, Land Economics/Use,