Factor-Augmenting Technical Change: An Empirical Assessment

This paper estimates factor-specific technical change and input substitution using a structural approach. It contributes to the existing literature by introducing various technology drivers for factor productivities and by assessing the impact of endogenous technical change on the elasticity of substitution. The empirical results suggest that factor productivities are indeed endogenous. In addition, technology drivers are factor-specific. Whereas the R&D stock and machinery imports are important determinants of energy and capital productivity, the education stock is statistically related to labour productivity. The rate of energy-augmenting technical change is larger than that of either labour or capital. By contrast, the productivity of these two factors grows at similar rates. Estimates of the elasticity of substitution are within the range identified by previous literature. In addition, we show that endogenous technical change reduces substitution. Because the elasticity of substitution is lower than one, knowledge and human capital can ultimately have an energy-using effect. The estimated structure of endogenous technical change suggests that Integrated Assessment models focusing on energy-saving technical change might underestimate climate policy costs.     

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.     

Environmental assessment and investment strategies of provincial industrial sector in China — Analysis based on DEA model

As an energy-intensive industry, the industrial sector consumes 70% of energy consumption and causes serious environmental pollution in China. Also, the government emphasized the promotion of R&D investment in the industrial sector in China's National Plan on Climate Change (2014–2020). It is meaningful and contributes to assessing energy and environmental performance, as well as R&D and industrial pollution control (IPC) investment strategies of China's industrial sector. A non-radial DEA model, as with natural and managerial disposability, was adopted to evaluate this from provincial and regional perspectives during the 2008–2012 period. Energy and environmental performance was evaluated by unified efficiency under natural disposability (UEN), unified efficiency under managerial disposability (UEM), and unified efficiency under natural and managerial disposability (UENM). The empirical results indicated that Shandong and Hainan were efficient under natural and managerial disposability, while other provinces had the potential to improve their energy and environmental performance. The number of provinces that was fit for investments of R&D and IPC increased from 2008 to 2010, then decreased in 2011 and 2012. In spite of this, many provincial industrial sectors should make efforts to reduce pollution by investment on technology. Tianjin, Heilongjiang, Jiangxi and Henan were especially the best investment objects because investments of R&D and IPC turned to be effective for them during the whole study period. Moreover, western China had the highest average UENM, followed by eastern China and central China. Eastern China and central China were rewarding to expand investments. Coal consumption was the main factor to negatively affect unified efficiency whereas the increase in economic development level was primarily responsible for the improvement of unified efficiency. According to the results, differentiated suggestions to further improve energy and environmental performance were proposed.     

The Value of Better Information on Technology R&D Programs in Response to Climate Change

Expert elicitations are a promising method for determining how R&D investments are likely to have an impact on technological advance in climate change energy technologies. But, expert elicitations are time consuming and resource intensive. Thus, we investigate the value of the information gained in expert elicitations. More specifically, given baseline elicitations from one study, we estimate the expected value of better information (EVBI) from revisiting and improving these assessments. We find that the EVBI is very large in comparison with the cost of performing expert elicitations. We also find that EVBI is higher on technologies with larger budgets and with net values that are not too high or too low.     

Prioritizing R&D for the U.S. Department of Energy's Weapons Complex Clean-Up

Prioritizing the U.S. Department of Energy's (DOE's) targeted R&D program supporting weapons-complex clean-up has been problematic. Considering issues common to all R&D evaluation, linking R&D prioritization to clean-up project prioritization, the latter aligned with institutional goals, offers a pragmatic solution. This method is illustrated with results of a clean-up prioritization model of DOE's Oak Ridge National Laboratory site.     

Evaluating the environmental sustainability of biomass-based energy strategy: Using an impact matrix framework

A roadmap for a more sustainable energy strategy is complex, as its development interacts critically with the economic, social, and environmental dimensions of sustainable development. This paper applied an impact matrix method to evaluate the environmental sustainability and to identify the desirable policy objectives of biomass-based energy strategy for the case of Alberta. A matrix with the sustainability domains on one axis and areas of environmental impact on the other was presented to evaluate the nexus effect of policy objectives and bioenergy production.As per to our analysis, economic diversification, technological innovation, and resource conservation came up as the desirable policy objectives of sustainable development for Alberta because they demonstrated environmental benefits in all environmental impact categories, namely climate change, human health, and ecosystem. On the other hand, human health and ecosystem impacts were identified as trade-offs when the policy objectives for sustainability were energy security, job creation, and climate change. Thus, bioenergy can mitigate climate change but may impact human health and ecosystem which then in turn can become issues of concern. Energy strategies may result in shifting of risks from one environmental impact category to another, and from one sustainable domain to another if the technical and policy-related issues are not identified.     

Influencing factors of carbon emissions in transportation industry based on CD function and LMDI decomposition model: China as an example

Globally, the transportation industry is one of the leading fields that generate the largest share of greenhouse gas emissions. While undergoing rapid development, countries worldwide aim to solve the problems involved in high energy consumption. Taking China as an example, this paper studies the main factors of carbon emissions in the transport sector and analyses the decoupling states between carbon emission and economic growth, making energy efficiency policies accordingly. In order to better demonstrate the dependence of the economy on the carbon emissions in China's transportation industry comprehensively, combined with the CD production function, this paper develops the decomposition and decoupling technology based on the LMDI approach. Additionally, it quantifies seven effects: energy emission intensity effect, energy structure effect, energy intensity effect, transportation intensity effect, technology state effect, labor input effect and capital input effect. The results show three major points: (1) From 2001 to 2018, the cumulative carbon emissions of China's transportation industry increased by 633.46 million tons, in which the capital input effect is the key factor driving carbon emissions, accounting for 157.70% of the total cumulative increased emissions, followed by energy structure effect at 10.39%. The labor input effect accounted for the smallest proportion at 2.26%. In this case, the technology state effect is the primary factor in restraining carbon emissions. During the study period, it reduced carbon emissions by 292.27 million tons, accounting for 46.14%. To a certain extent, energy intensity effect, transportation intensity effect and energy emission intensity inhibited carbon emissions, representing 16.67%, 5.32% and 2.22%, respectively. (2) During the research period, two decoupling states existed between carbon emissions and economic growth in China's transportation industry, specifically weak decoupling and expansive coupling. (3) The analysis of decomposition and decoupling state of influencing factors of carbon emissions shows that, on the one hand, factors promoting carbon emissions (capital input effect, energy structure effect and labor input effect) hinder the decoupling process. On the other hand, factors restraining carbon emissions (technology state effect, transportation intensity effect, energy intensity effect and energy emission intensity effect) accelerate the decoupling process. The research findings provide a new perspective for achieving carbon emission reduction in the transportation industry and curbing energy consumption growth.     

Tropical cyclone losses in the USA and the impact of climate change — A trend analysis based on data from a new approach to adjusting storm losses

Economic losses caused by tropical cyclones have increased dramatically. Historical changes in losses are a result of meteorological factors (changes in the incidence of severe cyclones, whether due to natural climate variability or as a result of human activity) and socio-economic factors (increased prosperity and a greater tendency for people to settle in exposed areas). This paper aims to isolate the socio-economic effects and ascertain the potential impact of climate change on this trend. Storm losses for the period 1950–2005 have been adjusted to the value of capital stock in 2005 so that any remaining trend cannot be ascribed to socio-economic developments. For this, we introduce a new approach to adjusting losses based on the change in capital stock at risk. Storm losses are mainly determined by the intensity of the storm and the material assets, such as property and infrastructure, located in the region affected. We therefore adjust the losses to exclude increases in the capital stock of the affected region. No trend is found for the period 1950–2005 as a whole. In the period 1971–2005, since the beginning of a trend towards increased intense cyclone activity, losses excluding socio-economic effects show an annual increase of 4% per annum. This increase must therefore be at least due to the impact of natural climate variability but, more likely than not, also due to anthropogenic forcings.     

Sustainable building assessment tool for project decision makers and its development process

The primary purpose of Sustainable Building Assessment (SBA) tools is for behavioral changes in public building practice. In the Web 3.0 era, even non-experts may have the capacity to select and use Information & Communication Technology (ICT) tools to solve complicated problems in sustainable building decision making without any expert help. It is clear that the R&D of these tools which directly target project decision makers is a significant project for researchers and policy makers who are tasked with the challenge of changing individual building practice for regional sustainability. The purpose of the study is to suggest a tool and its development process for sustainable building assessment by project decision makers, especially targeting non-expert groups. This study defines an SBA tool for project decision makers based on a typology and suggests a “3-layer development process framework”. For the theoretical Background, we integrate interdisciplinary methods and principles such as cognitive problem-solving and sustainable building delivery, including long-term community benefits in a region and an information system development process. This framework is based on Boehem's spiral model for information system development processes and especially emphasizes the first cycle to develop a core set of indicators fit for the non-expert user's problem solving process. This presents an iterative and gradual process with the aim of approaching the tool type from passive tools such as checklists and guidelines, to interactive software, especially interactive DSS (Decision Support Systems) online ICT platforms. The development process is comprised of 8 stages: This includes the use of mixed-method sequential design, including interviews, workshops, the Delphi survey technique, FD-AHP weight analysis, and scenario analysis. In addition, the study presents a case study of the application of the first cycle to develop a core set of indicators and discusses its effectiveness. This study may help researchers by providing a clear and effective method to understand interdisciplinary approaches to develop appropriate tools for sustainable building practice in a regional context.     

A comprehensive physico-chemical, mineralogical and morphological characterization of Indian mineral wastes

This paper provides a comprehensive characterization of mineral waste such as fly ash, bottom ash, slag and construction demolition (C&D) collected from four different thermal power plants, three steel plants and three C&D waste generation sites in India. To determine utilisation potential and environmental concerns, as received fly ash, bottom ash, slag and C&D waste were analysed for physico-chemical, mineralogical and morphological properties. The physico-chemical properties analysed include pH, moisture content, acid insoluble residue, loss on ignition(LOI), carbon content, fineness, chloride content, sulphate content, reactive silica content, XRF and heavy metal analysis. Morphological and mineralogical characteristics were investigated using scanning electron microscopy–energy dispersive X-ray. Particle size distribution was obtained using particle size analyser. The material analysed has different compositions and were selected with a view to determine their suitability for different applications in cement and concrete industry and for further research studies.     

Industrial transformation towards sustainability of the energy system

Human induced climate change is one of the single most significant indicators that human society is not pursuing a sustainable trajectory. Managing the risks requires a major transformation of the way energy needs are met. Such a transformation includes changes in the production and consumption system and the incentive structure that shapes this system. The major driving force for transformation is the public concern about the environmental impact of the present fossil fuel based energy system. We may expect that energy producers, encouraged by governments, NGOs and consumer preferences will be responding to these concerns and expectations sooner or later. In fact a number of major international energy companies are presently adjusting their strategies to the needs and concerns of the public. A mix of measures including energy efficiency, a switch to natural gas, major investments in low carbon and renewable energy technologies and underground storage of carbon are elements of such new strategies. Consumers in a number of OECD countries have expressed their willingness to pay more for energy, provided it is green and clean. NGOs continue to put pressure on governments to deal with the climate problem. The challenge for governments is to develop an institutional framework that helps the producers and consumers to go through a transformation of the energy system. As different groups in society are likely to support different strategies, this paper suggests that a pluralistic policy approach including efficiency standards, renewable energy portfolio standards, carbon taxes, and the introduction of a system of tradable emission permits is the most promising approach for a transformation towards a low carbon energy economy. Research can support a transformation of the energy system by exploring the various transformation scenarios. Such research should take a multi-disciplinary approach, it should focus on the energy system as a whole, including production, consumption and the incentive structure that shapes the interaction between the two and it should be international in scope. This revised version was published online in July 2006 with corrections to the Cover Date.     

Transboundary Pollution and Absorptive Capacity

The impact of the investment in absorptive capacity on transboundary pollution is studied by considering two countries, each of them regulating a firm. Firms can invest in inventive research and in absorptive research to lower their pollution intensity. The absorptive research enables a firm to capture part of the inventive research made by the other one. We show that by means of adequate emission taxes, original and absorptive research and development (R&D) subsidies, regulators can reach the non-cooperative social optimum. Interestingly, we show that the investment in absorptive research enables non-cooperating regulators to better internalize transboundary pollution. The higher is the learning parameter of absorption, the greater is the proportion of transboundary pollution internalized. Therefore, it is recommended for the international community to make the patent laws more flexible and enabling learning from the research made by others more interesting. Moreover, the investment in absorptive R&D may lead to multiple equilibria necessitating non-cooperating countries to coordinate on an equilibrium, which constitutes an incentive for them to cooperate.     

Issues in modeling induced technological change in energy, environmental, and climate policy

This paper addresses the objective of including Induced Technological Change (ITC) in research and policy models of energy, environment, and climate change. Theoretical foundations, fundamentals, and current methodologies for ITC models are reviewed. In addition, limitations and possible extensions to ITC models are explored. Current approaches to energy-environmental modeling that neglect technological characteristics such as heterogeneity, uncertainty, and path-dependence are likely to underestimate both the impact and the lags in the effectiveness of policy options.     

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.     

Assessing the impact of emissions trading scheme on low-carbon technological innovation: Evidence from China

A global consensus on carbon emission reductions has been reached for combating climate change. The Chinese government has clearly stated that it is necessary to make full use of market means to improve the level of environmental governance. Emissions trading scheme (ETS) is a typical market means to accelerate low-carbon economic transition. Low-carbon technological innovation is one of the key factors affecting carbon emissions. However, literature on the relationship between ETS and low-carbon technological innovation is relatively scarce at present. This study assesses the effect of pilot ETSs on low-carbon technological innovation, and a difference in differences (DID) model is adopted to analyze China's provincial panel data from 2003 to 2017. The results indicate that China's pilot ETSs can significantly promote low-carbon technological innovation, and changing the window period, PSM-DID and placebo test all verify the robustness of this finding. The dynamic effect test reveals that China's pilot ETSs will gradually increase the effect on low-carbon technological innovation over time. The heterogeneity analysis shows that the effect of China's pilot ETSs on low-carbon technological innovation is more obvious in Guangdong, Hubei, Tianjin and Chongqing. The mechanism analysis suggests that marketization degree and green consumption concept can positively moderate the impact of China's pilot ETSs on low-carbon technological innovation, and industrial structure upgrading plays a positive mediating role between China's pilot ETSs and low-carbon technological innovation. This study is conducive to assessing the policy effectiveness of China's pilot ETSs and provides an empirical evidence for promoting the development of the carbon emissions trading market.     

Combining Stochastic Optimization and Monte Carlo Simulation to Deal with Uncertainties in Climate Policy Assessment

In this paper, we explore the impact of several sources of uncertainties on the assessment of energy and climate policies when one uses in a harmonized way stochastic programming in a large-scale bottom-up (BU) model and Monte Carlo simulation in a large-scale top-down (TD) model. The BU model we use is the TIMES Integrated Assessment Model, which is run in a stochastic programming version to provide a hedging emission policy to cope with the uncertainty characterizing climate sensitivity. The TD model we use is the computable general equilibrium model GEMINI-E3. Through Monte Carlo simulations of randomly generated uncertain parameter values, one provides a stochastic micro- and macro-economic analysis. Through statistical analysis of the simulation results, we analyse the impact of the uncertainties on the policy assessment.     

Mitigation Costs in a Globalized World: Climate Policy Analysis with REMIND-R

Within this paper, we present the novel hybrid model REMIND-R and its application in a climate policy context based on the EU target to avoid a warming of the Earth’s atmosphere by more than 2°C compared to the pre-industrial level. This paper aims to identify necessary long-term changes in the energy system and the magnitude of costs to attain such a climate protection target under different designs of the post-2012 climate policy regime. The regional specification of mitigation costs is analyzed in the context of globalization where regions are linked by global markets for emission permits, goods, and several resources. From simulation experiments with REMIND-R, it turns out that quite different strategies of restructuring the energy system are pursued by the regions. Furthermore, it is demonstrated that the variance of mitigation costs is higher across regions than across policy regimes. First-order impacts, in particular, reduced rents from trade in fossil resources, prevail regardless of the design of the policy regime.     

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.     

Application of a checklist for quality assistance in environmental modelling to an energy model

Large, complex energy models present considerable challenges to develop and test. Uncertainty assessments of such models provide only partial guidance on the quality of the results. We have developed a model quality assistance checklist to aid in this purpose. The model checklist provides diagnostic output in the form of a set of pitfalls for the model application. The checklist is applied here to an energy model for the problem of assessing energy use and greenhouse gas emissions. Use of the checklist suggests that results on this issue are contingent on a number of assumptions that are highly value-laden. When these assumptions are held fixed, the model is deemed capable of producing moderately robust results of relevance to climate policy over the longer term. Checklist responses also indicate that a number of details critical to policy choices or outcomes on this issue are not captured in the model, and model results should therefore be supplemented with alternative analyses.     

The Effect of Green Investments in an Agent-Based Climate-Economic Model

Climate-economic modeling often relies on macroeconomic integrated assessment models (IAMs) that in general try to capture how the combined system reacts to different policies. Irrespective of the specific modeling approach, IAMs suffer from two notable problems. First, although policies and emissions are dependent on individual or institutional behavior, the models are not able to account for the heterogeneity and adaptive behavior of relevant actors. Second, the models unanimously consider mitigation actions as costs instead of investments: an arguable definition, given that all other expenditures are classified as investments. Both are challenging if the long-term development of climate change and the economy shall be analyzed. This paper therefore proposes a dynamic agent-based model, based on the battle of perspectives approach (Janssen [1]; Janssen and de Vries [2]; Geisendorf [3, 4]) that details the consequences of various behavioral assumptions. Furthermore, expenditures for climate protection, e.g., the transition of the energy system to renewables, are regarded as investments in future technologies with promising growth rates and the potential to incite further growth in adjoining sectors (Jaeger et al. [5]). The paper analyzes how a different understanding of climate protection expenditures changes the system’s dynamic and, thus, the basis for climate policy decisions. The paper also demonstrates how erroneous perceptions impact on economic and climate development, underlining the importance to acknowledge heterogeneous beliefs and behavior for the success of climate policy.