Scientist–stakeholder collaboration in integrated assessment of climate change: lessons from a case study of Northwest Canada

Research on the climate change issue has generally focused on uncertainties in climate projections and calculation of mitigation costs. Most integrated assessment (IA) efforts have been directed at the mitigation component. The problem of climate change, however, is really about the potential effects on ecosystems, resources, and societies that depend on them. As illustrated by experiences from the Mackenzie Basin Impact Study, a case study from Northwest Canada, these effects will be unique to each region and country. Many of these will be indirect, including region specific thresholds, vulnerabilities and adaptations which may not be included in sectoral analyses. Science can define some of the “What if” aspects of climate change, but the regional “So what” and “What should be done” questions are largely unanswered and subject to intense debate in various political fora. IA needs to recognize the multiobjective and multistakeholder aspects of vulnerabilities, risks, and potential responses to climate change. IA could provide a more holistic analysis of the regional impacts dimension of climate change by including both modeling and non‐modeling approaches, and incorporating institutional and stakeholder issues that do not readily lend themselves to economic analyses.     

Human Capital,Innovation, and Climate Policy: an Integrated Assessment

This paper looks at the interplay between human capital and innovation when climate and educational policies are implemented. Following recent empirical studies, human capital and general purpose research and development (R&D) are introduced in an integrated assessment model used to study the dynamics of climate change mitigation. Our results suggest that climate policy stimulates general purpose as well as clean R&D but reduces the incentive to invest in human capital formation. Both innovation and human capital have a scale effect, which increases pollution, as well as a technique effect, which saves emissions for each unit of output produced. While the energy-saving effect prevails when innovation increases, human capital is pollution-using, also because of the gross complementarity between the labor and energy input. When the role of human capital is the key input in the production of general purpose and energy knowledge is accounted for, the crowding-out of education induced by climate policy is mitigated, though not completely offset. By contrast, a policy mix that combines educational as well as climate objectives offsets the human capital crowding-out, at moderate and short-term costs. Over the long run, the policy mix leads to global welfare gains.     

On the optimal control of carbon dioxide emissions: an application of FUND

This paper presents the Climate Framework for Uncertainty, Negotiation and Distribution (FUND), an integrated assessment model of climate change, and discusses selected results. FUND is a nine‐region model of the world economy and its interactions with climate, running in time steps of one year from 1990 to 2200. The model consists of scenarios for economy and population, which are perturbed by climate change and greenhouse gas emission reduction policy. Each region optimizes its net present welfare. Policy variables are energy and carbon efficiency improvement, and sequestering carbon dioxide in forests. It is found that reducing conventional air pollution is a major reason to abate carbon dioxide emissions. Climate change is an additional reason to abate emissions. Reducing and changing energy use is preferred as an option over sequestering carbon. Under non‐cooperation, free riding as well as assurance behaviour is observed in the model. The scope for joint implementation is limited. Under cooperation, optimal emission abatement is (slightly) higher than under non‐cooperation, but the global coalition is not self‐enforcing while side payments are insufficient. Optimal emission control under non‐cooperation is less than currently discussed under the Framework Convention on Climate Change, but higher than observed in practice. This revised version was published online in July 2006 with corrections to the Cover Date.     

Mitigation Portfolio and Policy Instruments When Hedging Against Climate Policy and Technology Uncertainty

In this paper, we use a stochastic integrated assessment model to evaluate the effects of uncertainty about future carbon taxes and the costs of low-carbon power technologies. We assess the implications of such ambiguity on the mitigation portfolio under a variety of assumptions and evaluate the role of emission performance standards and renewable portfolios in accompanying a market-based climate policy. Results suggest that climate policy and technology uncertainties are important with varying effects on all abatement options. The effect varies with the technology, the type of uncertainty, and the level of risk. We show that carbon price uncertainty does not substantially change the level of abatement, but it does have an influence on the mitigation portfolio, reducing in particular energy R&D investments in advanced technologies. When investment costs are uncertain, investments are discouraged, especially during the early stages, but the effect is mitigated for the technologies with technological learning prospects. Overall, these insights support some level of regulation to encourage investments in coal equipped with carbon capture and storage and clean energy R&D.     

“Emission game”: some applications of the theory of games to the problem of CO2 emission

If there are no doubts that we must reduce the total emission of carbon dioxide, then the problem of how much different countries should be allowed to contribute to this amount remains a serious one. We suggest this problem to be considered as a non-antagonistic game (in Germeier's sense). A game of this kind is called an “emission game”. Suppose that there are n independent actors (countries or regions), each of them releasing a certain amount of CO₂ per year (in carbon units) into the atmosphere, and that the emission would be reduced by each actor. Each actor has his own aim: to minimise the loss in the Gross Domestic Product (GDP) caused by the reduction of emissions. On the other hand, taking into account that it is impossible to estimate more or less precisely the impact of the climate change on GDP for each country today, a common strategy will be to reduce the climate change. Since one of the main leading factors in global warming is the greenhouse effect, then the common aim will be to reduce the sum of emissions. This is a typical conflict situation. How to resolve it? We can weigh the “egoistic” and “altruistic” criteria for each actor introducing the so-called “coefficients of egoism”. This coefficient is very large, if the actor uses a very egoistic strategy, and conversely, if the actor is a “super-altruist”, then the corresponding coefficient is very small. Using these coefficients we get the general solution of the game in a form of some Pareto's equilibrium. The solution is stable and efficient.     

An integrated assessment modeling framework for assessing primary and secondary impacts from carbon dioxide stabilization scenarios

As the nations of the world negotiate future controls on greenhouse gas emissions, a critical environmental policy issue becomes understanding the multiple environmental consequences of these controls. Here we describe an integrated assessment model for quantifying multiple environmental impacts of large-scale environmental initiatives and apply this model to climate change mitigation. Our analysis shows that reductions in global warming will be accompanied by reductions in ozone depletion, acid rain and mercury emissions, and desulfurization waste generation. We also conclude that the largest collateral benefits from reducing global climate change may be in the developing world. This result is critical since it is the developing nations who ultimately control the long-term success of any climate stabilization strategy. This revised version was published online in July 2006 with corrections to the Cover Date.     

INASUD project findings on integrated assessment of climate policies

This communication summarizes the main findings of INASUD, an European-wide research project on integrated assessment of climate policies. The project aimed at improving the framing of climate policy analysis through the parallel use of various existing integrated assessment models. It provides a comprehensive examination of the link between uncertainty regarding damages and inertia in economic systems. Results show that the Kyoto targets and timing are consistent with the precautionary principle but offers little insurance for longer-term climate protection. Flexibility mechanisms offer potentials for cooperation with developing countries, and are necessary to tap the environmental and economic benefits of joint carbon and sulfur emissions abatement.     

Climate Damage on Production or on Growth: What Impact on the Social Cost of Carbon?

Recent articles have investigated with integrated assessment models the possibility that climate damage bears on productivity (TFP) growth and not on production. Here, we compare the impact of these alternative representations of damage on the social cost of carbon (SCC). We ask whether damage on TFP growth leads to higher SCC than damage on production ceteris paribus. To make possible a controlled comparison, we introduce a measure of aggregate damage, or damage strength, based on welfare variations. With a simple climate-economy model, we compare three damage structures: quadratic damage on production, linear damage on growth and quadratic damage on growth. We show that when damage strength is the same, the ranking of SCC between a model with damage on production and a model with damage on TFP growth is not unequivocal. It depends on welfare parameters such as the utility discount rate or the elasticity of marginal social utility of consumption.     

Assessing the urban carbon footprint: An overview

All cities present environmental sustainability issues, above all regarding greenhouse gas (GHG) emissions, and specifically carbon dioxide (CO₂), that directly affect climate change. Consequently, it is very important to quantify and report their Carbon Footprint (CF) for implementing national and international policies/strategies aimed at mitigating and adapting these concerns. The Urban Carbon Footprint (UCF), indeed, has been recognized as the more valuable choice to inform, specifically, decision makers about city environmental sustainability. Several accounting systems and inventory methods have been taken into account to perform UCF, highlighting the complexity of the topic and generating very often confusion among users.In this context, the authors aim to summarize what has been done and what is going on with UCFs, trying to classify them according to some principal dimensions. Thus, they divide UFCs in two main categories namely: “spatial” or “direct”, with a limited amount of data requested, and “economic” or “life cycle based”, more or less data inclusive according to the accounting systems considered. Furthermore, they observe that there is not a “global agreed-upon protocol” yet, neither is there a specific model shared among researchers, even if some steps have been made towards this direction (Relative Carbon Footprint - RCF, Publicly Available Specification – PAS 2070 and Global Protocol for Community scale - GPC). Consequently, it is necessary to complete and standardize, in the short term, the accounting and reporting frameworks, in order to compare different UCFs for adopting shared climate strategies and actions at global level.     

The Contribution of Mathematical Models to Climate Policy Design: a Researcher’s Perspective

Energy and the environment are closely interconnected. In particular, energy-related carbon dioxide emissions are major contributors to climate change. To analyze options within the energy sector to curb greenhouse gas emissions, or to study alternative climate strategies such as adaptation and geoengineering measures, policy-makers can rely on mathematical decision support models, in particular E3 (economy/energy/environment) models and integrated assessment models (IAMs). This paper reviews some of my recent contributions to climate policy design using different types of E3 models and IAMs.     

Assessing the Impact of Carbon Tax Differentiation in the European Union

To what extent do the welfare costs associated with the implementation of the Burden Sharing Agreement in the European Union depend on sectoral allocation of emissions rights? What are the prospects for strategic climate policy to favor domestic production? This paper attempts to answer those questions using a CGE model featuring a detailed representation of the European economies. First, numerical simulations show that equalizing marginal abatement costs across domestic sectors greatly reduces the burden of the emissions constraint but also that other allocations may be preferable for some countries because of pre-existing tax distortions. Second, we show that the effect of a single country's attempt to undertake a strategic policy to limit impacts on its domestic energy-intensive industries has mixed effects. Exempting energy-intensive industries from the reduction program is a costly solution to maintain the international competitiveness of these industries; a tax-cum-subsidy approach is shown to be better than exemption policy to sustain exports. The welfare impact either policy – exemption or subsidy – on other European countries is likely to be small because of general equilibrium effects.     

Assessing the impact of FDI on PM2.5 concentrations: A nonlinear panel data analysis for emerging economies

While emerging markets have obtained powerful growth for foreign direct investment (FDI) inflows, they are facing severe smog pollution, and this contradiction has become increasingly prominent since the financial crisis. Assessing the influence of FDI on pollutant emissions is of great significance for determining how to attract FDI to promote environmental sustainability. The present study simultaneously investigates the direct and indirect effects of FDI on PM_2.5 contamination for emerging countries spanning the period 2010–2016. Due to the features of the nonlinear analysis, a generalized panel smooth transition regression (GPSTR) model was introduced, and cross-sectional dependence, heterogeneity, nonlinear unit root, nonlinear cointegration tests and non-parametric kernel density estimation were applied to achieve this goal. The results reveal that FDI directly contributes to decreasing PM_2.5, but indirectly has on increasing PM_2.5 emissions. The total effect of FDI on PM_2.5 concentrations is proven to be negative, which confirms the pollution halo hypothesis. Moreover, the connection between FDI inflows and PM_2.5 emissions displays a threshold and dynamic characteristic and is “S-shaped”. At lower levels of FDI, the inflows of FDI exert a positive effect on reducing PM_2.5 concentrations, whereas when FDI exceeds the threshold of 23.2981, such influence is gradually weakened with an increase in its own accumulation. The study provides new assessments on FDI's contribution to pollutant emissions and evidence for environmental sustainability in the post-financial crisis era.     

Carbon Emissions Trading and Equity in International Agreements

This paper explores the distributional consequences of alternative emissions trading schemes. It is argued that the distributional impact stems from the difference between two social welfare functions: the function which is implicitly maximised in a competitive market equilibrium and the function which is implicitly adopted when a given equity principle is chosen. An integrated assessment model is used to illustrate these findings, by simulating the introduction of various emissions trading schemes for the Kyoto protocol on the reduction of greenhouse emissions. It is shown that (1) changes produced on an equity index by the imposition of emission constraints (by country) may not be significantly higher than those obtained by the subsequent introduction of a market mechanism, and that (2) the various market regimes which could be adopted have quite different distributional implications. This revised version was published online in July 2006 with corrections to the Cover Date.     

Approaches for performing uncertainty analysis in large-scale energy/economic policy models

A number of key policy insights have emerged from the application of large-scale economic/energy models, such as integrated assessment models for climate change. These insights have been particularly powerful in those instances when they are shared by all or most of the existing models. On the other hand, some results and policy recommendations obtained from integrated assessment models vary widely from model to model. This can limit their usability for policy analysis. The differences between model results are mostly due to different underlying assumptions about exogenous processes, about endogenous processes and the dynamics among them, differences in value judgments, and different approaches for simplifying model structure for computational purposes. Uncertainty analyses should be performed for the dual purpose of clarifying the uncertainties inherent in model results and improving decision making under uncertainty. This paper develops a unifying framework for comparing the different types of uncertainty analyses through their objective functions, categorizes types of uncertainty analyses that can be performed on large models, and compares different approaches to uncertainty analysis by explaining underlying assumptions, suitability for different model types, and advantages and disadvantages. The appendix presents a summary of integrated assessment models for climate change that explicitly account for uncertainty.     

Is Globalization Detrimental to CO<Subscript>2</Subscript> Emissions in Japan? New Threshold Analysis

Using annual data from 1970 to 2014, this paper examines the effects of globalization on CO₂ emissions in Japan while accounting for economic growth and energy consumption as potential determinants of carbon emissions. The structural breaks and asymmetries arising due to policy shifts require attention, and hence, an asymmetric threshold version of the ARDL model is utilized. The results show the presence of threshold asymmetric cointegration between variables. Threshold-based positive and negative shocks arising from globalization increase carbon emissions, while the impact of the latter is more profound. Energy consumption (economic growth) also has a significant positive effect on carbon emissions. Globalization, economic growth, and energy consumption significantly increase carbon emissions in the short run. We suggest that policy makers in Japan consider globalization and energy consumption as policy tools in formulating their policies regarding protecting sustainable environmental quality in the long run. Otherwise, the Japanese economy may continue to face environmental consequences such as undesirable climate change and massive warming at the micro and macro levels as a result of potential shocks arising from globalization and energy consumption.     

Does the high–tech industry consistently reduce CO2 emissions? Results from nonparametric additive regression model

China is currently the world's largest carbon dioxide (CO₂) emitter. Moreover, total energy consumption and CO₂ emissions in China will continue to increase due to the rapid growth of industrialization and urbanization. Therefore, vigorously developing the high–tech industry becomes an inevitable choice to reduce CO₂ emissions at the moment or in the future. However, ignoring the existing nonlinear links between economic variables, most scholars use traditional linear models to explore the impact of the high–tech industry on CO₂ emissions from an aggregate perspective. Few studies have focused on nonlinear relationships and regional differences in China. Based on panel data of 1998–2014, this study uses the nonparametric additive regression model to explore the nonlinear effect of the high–tech industry from a regional perspective. The estimated results show that the residual sum of squares (SSR) of the nonparametric additive regression model in the eastern, central and western regions are 0.693, 0.054 and 0.085 respectively, which are much less those that of the traditional linear regression model (3.158, 4.227 and 7.196). This verifies that the nonparametric additive regression model has a better fitting effect. Specifically, the high–tech industry produces an inverted “U–shaped” nonlinear impact on CO₂ emissions in the eastern region, but a positive “U–shaped” nonlinear effect in the central and western regions. Therefore, the nonlinear impact of the high–tech industry on CO₂ emissions in the three regions should be given adequate attention in developing effective abatement policies.     

Climatic Implications of Revised IPCC Emissions Scenarios, the Kyoto Protocol and Quantification of Uncertainties

In December 1997, the United Nations Framework Convention on Climate Change (FCCC) adopted the Kyoto Protocol. This paper describes a framework that models the climatic implications of this international agreement, using Monte Carlo simulations and the preliminary Intergovernmental Panel on Climate Change emissions scenarios (SRES). Emissions scenarios (including intervention scenarios), climate sensitivity, and terrestrial carbon sink are the key sampled model parameters. This framework gives prior probability distributions to these parameters and, using a simple climate model, posterior distributions of global temperature change are determined for the future. Our exercise showed that the Kyoto Protocol's effectiveness will be mostly dependent upon which SRES world evolves. In some worlds the Protocol decreases the warming considerably but in others it is almost irrelevant. We exemplified this approach with a current FCCC issue, namely “hot air”. This modelling framework provides a probabilistic assessment of climate policies, which can be useful for decision-makers involved in global climate change management. This revised version was published online in July 2006 with corrections to the Cover Date.     

Assessment of groundwater quality: a fusion of geochemical and geophysical information via Bayesian neural networks

Deplorable quality of groundwater arising from saltwater intrusion, natural leaching and anthropogenic activities is one of the major concerns for the society. Assessment of groundwater quality is, therefore, a primary objective of scientific research. Here, we propose an artificial neural network-based method set in a Bayesian neural network (BNN) framework and employ it to assess groundwater quality. The approach is based on analyzing 36 water samples and inverting up to 85 Schlumberger vertical electrical sounding data. We constructed a priori model by suitably parameterizing geochemical and geophysical data collected from the western part of India. The posterior model (post-inversion) was estimated using the BNN learning procedure and global hybrid Monte Carlo/Markov Chain Monte Carlo optimization scheme. By suitable parameterization of geochemical and geophysical parameters, we simulated 1,500 training samples, out of which 50 % samples were used for training and remaining 50 % were used for validation and testing. We show that the trained model is able to classify validation and test samples with 85 % and 80 % accuracy respectively. Based on cross-correlation analysis and Gibb’s diagram of geochemical attributes, the groundwater qualities of the study area were classified into following three categories: “Very good”, “Good”, and “Unsuitable”. The BNN model-based results suggest that groundwater quality falls mostly in the range of “Good” to “Very good” except for some places near the Arabian Sea. The new modeling results powered by uncertainty and statistical analyses would provide useful constrain, which could be utilized in monitoring and assessment of the groundwater quality.     

Evaluating Uncertain CO2 Abatement over the Very Long Term

Climate change research with the economic methodology of cost–benefit analysis is challenging because of valuation and ethical issues associated with the long delays between CO₂ emissions and much of their potential damages, typically of several centuries. The large uncertainties with which climate change impacts are known today and the possibly temporary nature of some envisaged CO₂ abatement options exacerbate this challenge. For example, potential leakage of CO₂ from geological reservoirs, after this greenhouse gas has been stored artificially underground for climate control reasons, requires an analysis in which the uncertain climatic consequences of leakage are valued over many centuries. We here present a discussion of some of the relevant questions in this context and provide calculations with the top–down energy-environment-economy model DEMETER. Given the long-term features of the climate change conundrum as well as of technologies that can contribute to its solution, we considered it necessary extending DEMETER to cover a period from today until the year?3000, a time span so far hardly investigated with integrated assessment models of climate change.     

Meta-Modeling to Assess the Possible Future of Paris Agreement

In the meta-modeling approach, one builds a numerically tractable dynamic optimization or game model in which the parameters are identified through statistical emulation of a detailed large scale numerical simulation model. In this paper, we show how this approach can be used to assess the economic impacts of possible climate policies compatible with the Paris Agreement. One indicates why it is appropriate to assume that an international carbon market, with emission rights given to different groups of countries will exist. One discusses the approach to evaluate correctly abatement costs and welfare losses incurred by different groups of countries when implementing climate policies. Finally, using a recently proposed meta-model of game with a coupled constraint on a cumulative CO₂ emissions budget, we assess several new scenarios for possible fair burden sharing in climate policies compatible with the Paris Agreement.