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

Uncertain R&D, Backstop Technology and GHGs Stabilization

This paper analyses optimal investments in innovation when dealing with a stringent climate target and with the uncertain effectiveness of R&D. The innovation needed to achieve the deep cut in emissions is modelled by a backstop carbon-free technology whose cost depends on R&D investments. To better represent the process of technological progress, we assume that R&D effectiveness is uncertain. By means of a simple analytical model, we show how accounting for the uncertainty that characterizes technological advancement yields higher investments in innovation and lower policy costs. We then confirm the results via a numerical analysis performed with a stochastic version of WITCH, an energy-economy-climate model. The results stress the importance of a correct specification of the technological change process in economy-climate models.Climate Change, Information and Uncertainty, Environmental Policy, Optimal R&D Investments

Carbon capture and sequestration: how much does this uncertain option affect near-term policy choices?

Policy makers as well as many economists recognize geological Carbon Capture and Sequestration (CCS) as a key option to avoid costly emission reduction. While an extreme perspective is to envision CCS as a magic bullet to solve the issue of climate change, the economics perspective is more balanced and see it as a part of a portfolio of mitigation actions. Besides, as any novel mitigation technology, CCS can be implemented with a twofold purpose; on one side it can substitute some other technological efforts to reach a given environmental target. On the other side, it offers the opportunity to go for additional emission reductions andreach a "safer" climate target. In order to balance these twopossible utilizations of CCS and assess their respective effects onearly policystrategies, we undertake a twofold numerical experiment. First, a cost-efficiency analysis is undertaken where CCS sole effect is substitution of other efforts. This is followed by a cost-benefit analysis where both purposes have to be balanced. We find that future availability of CCS is less a reason to relax near-term abatement efforts than what could be inferred from previous analyses. Moreover, cost-benefit analysis indicates that the environmental target should be more ambitious when CCS is included in the picture.Climate Change, Uncertainty, Sequestration, Cost-benefit analysis

Sustainable Cooperation in Global Climate Policy: Specific Formulas and Emission Targets to Build on Copenhagen and Cancun

We offer a framework to assign quantitative allocations of emissions of greenhouse gases (GHGs), across countries, one budget period at a time. Under the two-part plan: (i) China, India, and other developing countries accept targets at Business as Usual (BAU) in the coming budget period, the same period in which the US first agrees to cuts below BAU; and (ii) all countries are asked in the future to make further cuts in accordance with a common numerical formula to all. The formula is expressed as the sum of a Progressive Reductions Factor, a Latecomer Catch-up Factor, and a Gradual Equalization Factor. This paper builds on our previous work in many ways. First we update targets to reflect pledges made by governments after the Copenhagen Accord of December 2010 and confirmed at the Cancun meeting of December 2011. Second, the WITCH model, which we use to project economic and environmental effects of any given set of emission targets, has been refined and updated to reflect economic and technological developments. We include the possibility of emissions reduction from bio energy (BE), carbon capture and storage (CCS), and avoided deforestation and forest degradation (REDD+) which is an important component of pledges in several developing countries. Third, we use a Nash criterion for evaluating whether a country’s costs are too high to sustain cooperation.Cancun, Climate, Concentrations, Cooperation, Copenhagen, Costs, Developing Countries, Development, Emissions, Equity, Global Climate, Global Warming, Greenhouse Gas, Human Development, International, Kyoto, Sustainable, Treaty, United Nations, WITCH

Global Climate Policy Architecture and Political Feasibility: Specific Formulas and Emission Targets to Attain 460 ppm CO2 Concentrations

Three gaps in the Kyoto Protocol most badly need to be filled: the absence of emission targets extending far into the future, the absence of participation by the United States, China, and other developing countries, and the absence of reason to think that members will abide by commitments. To be politically acceptable, any new treaty that fills these gaps must, we believe, obey certain constraints regarding country-by-country economic costs. We offer a framework of formulas that assign quantitative allocations of emissions, across countries, one budget period at a time. The two-part plan: (i) China and other developing countries accept targets at BAU in the coming budget period, the same period in which the US first agrees to cuts below BAU; and (ii) all countries are asked in the future to make further cuts in accordance with a formula which sums up a Progressive Reductions Factor, a Latecomer Catch-up Factor, and a Gradual Equalization Factor. An earlier proposal for specific parameter values in the formulas – Frankel (2009), as analyzed by Bosetti, et al (2009) – achieved the environmental goal that concentrations of CO2 plateau at 500 ppm by 2100. It succeeded in obeying our political constraints, such as keeping the economic cost for every country below the thresholds of Y=1% of income in Present Discounted Value, and X=5% of income in the worst period. In pursuit of more aggressive environmental goals, we now advance the dates at which some countries are asked to begin cutting below BAU, within our framework. We also tinker with the values for the parameters in the formulas. The resulting target paths for emissions are run through the WITCH model to find their economic and environmental effects. We find that it is not possible to attain a 380 ppm CO2 goal (roughly in line with the 2°C target) without violating our political constraints. We were however, able to attain a concentration goal of 460 ppm CO2 with looser political constraints. The most important result is that we had to raise the threshold of costs above which a country drops out, to as high as Y =3.4% of income in PDV terms, or X =12 % in the worst budget period. Some may conclude from these results that the more aggressive environmental goals are not attainable in practice, and that our earlier proposal for how to attain 500 ppm CO2 is the better plan. We take no position on which environmental goal is best overall. Rather, we submit that, whatever the goal, our approach will give targets that are more practical economically and politically than approaches that have been proposed by others.International Climate Agreements

Fondazione Eni Enrico Mattei

This paper develops and tests a dynamic optimization model of fishermen’s investment behavior in a limited-entry fishery. Because exit from limited-entry fisheries may be irreversible, the fisherman has an incentive to maintain the right to fish (whether by actually fishing or by purchasing an annual license) even when the fishery is not profitable, in the hope that conditions may improve. This incentive provides at least a partial explanation for excess capacity in fishing fleets, one of the most pressing fisheries management issues in limited-entry (and other) fisheries around the world. To assess the ability of simple financial models to explain observed investment behavior, we develop a two-factor (price and catch) real options model of the decision problem faced by an active fisherman who has the option to exit a fishery irrevocably. The immediate reason for adopting a two-factor model is the hope of achieving greater predictive power, since obviously both price and catch are important to fishermen’s decisions. Another advantage to this approach is that it provides a mechanism by which investment behavior can be linked in a real options framework to exogenous factors that affect price and catch separately. For example, international market forces are likely to affect price while having a negligible effect on a local fish stock, while local fish stock dynamics may affect catch directly but have little influence on prices (assuming the demand for a particular fish is relatively elastic). In a comparison of model predictions about fishermen’s exit decisions to 5059 observed decisions in the California salmon fishery in the 1990s, 65% of the model’s predictions are correct, suggesting this approach may be useful in the analysis of fishing fleet dynamics.Real option investment, Numerical methods, Fisheries

Carbon Capture and Sequestration: How Much Does this Uncertain Option Affect Near-Term Policy Choices?

One of the main issues in the climate policy agenda, the timing of abatement efforts, hinges on the uncertainties of climate change risks and technological evolution. We use a stochastic optimization framework and jointly explore these two features. First, we embed in the model future potential large-scale availability of Carbon Capture and Storage (CCS) technologies. While non-CCS mitigation that reduces fossil energy use is modelled as exerting inertia on the economic system, mainly due to the durability of the capital in energy systems and to technology lock-in and lock-out phenomena, the implementation of CCS technologies is modelled as implying less resilience of the system to changes in policy directions. Second, climate uncertainty is related in the model to the atmospheric temperature response to an increase in GHGs concentration. Performing different simulation experiments, we find that the environmental target, derived from a cost-benefit analysis, should be more ambitious when CCS is included in the picture. Moreover, the possible future availability of CCS is not a reason to significantly reduce near-term optimal abatement efforts. Finally, the availability of better information on the climate cycle is in general more valuable than better information on the CCS technological option.Climate change, Uncertainty, Sequestration, Cost-benefit analysis

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

A Data Envelopment Analysis Approach to the Assessment of Natural Parks’ Economic Efficiency and Sustainability. The Case of Italian National Parks

Wilderness protection is a growing necessity for modern societies, and this is particularly true for areas where population density is extremely high, as for example Europe. Conservation, however, implies very high opportunity costs. It is thus crucial to create incentives to efficient management practices, to promote benchmarking and to improve conservation management. In the present paper we propose a methodology based on Data Envelopment Analysis, DEA, a non parametric benchmarking technique specifically developed to assess the relative efficiency of decision-making units. In particular, the objective of the discussed methodology is to assess the relative efficiency of the management units of the protected area and to indicate how it could be improved, by providing a set of guidelines. The main advantage of this methodology is that it allows to assess the efficiency of natural parks’ management not only internally (comparing the performance of the park to itself in time) but also by external benchmarking, thus providing new and different perspectives on potential improvements. Although the proposed methodology is fairly general, we have applied it to the context of Italian National Parks in order to produce a representative case study. Specifically, the choice of adequate cost and benefit indicators is a very important and delicate phase of any benchmark analysis. For this purpose, a questionnaire was used to investigate the opinions of Italian National Parks managers and stakeholders and to define the relevant indicators for the analysis. Finally, relevant policy implications for the case study are given.Data envelopment analysis, Natural park management

Politics and Economics of Second-Best Regulation of Greenhouse Gases: The Importance of Regulatory Credibility

Modellers have examined a wide array of ideal-world scenarios for regulation of greenhouse gases. In this ideal world, all countries limit emissions from all economic sectors; regulations are implemented by intelligent, well-informed forward-looking agents; all abatement options, such as new energy technologies and forestry offsets, are available; trade in goods, services and emission credits is free and unfettered. Here we systematically explore more plausible second-best worlds. While analysts have given inordinate attention to which countries participate in regulation—what we call “variable geometry”—which has a strikingly small impact on total world cost of carbon regulations if international trade in emission credits allows economies to equilibrate. Limits on emission trading raise those costs, but by a much smaller amount than expected because even modest amounts of emission trading (less than 15% of abatement in a plausible scenario that varies the geometry of effort) have a large cost-reducing impact. Second best scenarios that see one sector regulated more aggressively and rapidly than others do not impose much extra burden when compared with optimal all-sector scenarios provided that regulations begin in the power sector. Indeed, some forms of trade regulation might decrease the financial flows associated to a carbon policy thus increasing political feasibility of the climate agreement. Much more important than variable geometry, trading and sectors is another factor that analysts have largely ignored: credibility. In the real world governments find it difficult to craft and implement credible international regulations and thus agents are unable to be so forward-looking as assumed in ideal-world modelling exercises. As credibility declines the cost of coordinated international regulation skyrockets—even in developing countries that are likely to delay their adoption of binding limits on emissions. Because international institutions such as treaties are usually weak, governments must rely on their own actions to boost regulatory credibility—for example, governments might “pre-commit” international regulations into domestic law before international negotiations are finally settled, thus boosting credibility. In our scenarios, China alone would be a net beneficiary of pre-commitment that advances its carbon limits two decades (from 2030, in our scenario, to today) if doing so would make international regulations more credible and thus encourage Chinese firms to invest with a clearer eye to the future. Overall, low credibility is up to 6 times more important in driving higher world costs for carbon regulations when compared with variable geometry, limits on emission trading and variable sectors. In this paper, we have not explored the other major dimension to the second-best: the lack of timely availability of the full range of abatement options, although our results suggest that even this will be less consequential than credibility.Greenhouse Gases, Second-best Regulation