Greenhouse Gas Mitigation Scenarios Research Articles

Greenhouse gas mitigation scenarios in the solid waste sector for compliance with the Brazilian NDC: Case study of the Recife metropolitan area, Brazil.

The Intergovernmental Panel on Climate Change (IPCC) indicates that the waste sector is a potential emitter of methane gas (CH4), which has a greenhouse effect up to 28 times greater than that of carbon dioxide (CO2). The management of municipal solid waste (MSW) generates greenhouse gases (GHG) directly through emissions from the process itself as well as indirectly through transportation and energy consumption. The objective of this study was to evaluate the GHG emissions contributed by the waste sector in the Recife metropolitan region (RMR) and to define mitigation scenarios to comply with the Brazilian Nationally Determined Contribution (NDC), a result of the Paris Agreement. To achieve this, an exploratory study was carried out, including a literature review, collection of data, estimation of emissions using the IPCC model (2006), and comparison between the values assumed by the country in 2015 and those estimated in the adopted mitigation scenarios. The RMR is composed of 15 municipalities, has an area of 3,216,262 km2 and a population of 4,054,866 inhabitants (2018), generating approximality 1.4 million t-year of MSW. It was estimated that, in the period from 2006 to 2018, 25.4 million tCO2e were emitted. The comparative analysis between the absolute values defined in the Brazilian NDC and the results from the mitigation scenarios showed that approximately 36 million tCO2e could be avoided through the disposal of MSW in the RMR, equivalent to a 52% reduction in emissions estimated for 2030, a percentage greater than the 47% reduction assumed in the Paris Agreement.

Scaling up climate ambition post-2030: a long-term GHG mitigation analysis for Thailand

ABSTRACT Thailand’s Nationally Determined Contribution (NDC) submitted to the United Nations Framework Convention on Climate Change (UNFCCC) aims to reduce 20 to 25% of greenhouse gas (GHG) emissions with respect to the projected reference level of NDC in 2030, respectively, in its unconditional and conditional scenarios. The Intergovernmental Panel on Climate Change (IPCC) states that limiting global temperature rise to 1.5°C would require net zero carbon dioxide emissions globally by around 2050. Thailand’s current energy system is highly fossil fuel dependent and requires enormous transformations to achieve more stringent GHG emission reduction targets beyond its NDC. This paper seeks to estimate the level and the intensities of Thailand’s energy system and their economy-wide effects post-2030 under the business as usual and 16 GHG emission reduction scenarios ranging from 30 to 100% by 2050. A computable general equilibrium analysis using the AIM/Hub model is employed to estimate the macroeconomic impacts of meeting the unconditional and conditional emission reductions of Thailand’s NDC in 2030 along with varying GHG emission reductions in 2050. Results show that renewables – constituting solar, wind, biomass and hydro and carbon capture and storage (CCS) technologies account for more than 95% in the power generation mix by 2050, if 100% GHG emission reduction from the 2010 level is to be achieved. Electricity generation based on biomass both with and without CCS will occupy a major share in the investments by 2050 in all the conditional and unconditional NDC scenarios. A rapid increase in carbon sequestration occurs from 2040 onwards through the deployment of CCS and bioenergy with CCS (BECCS) technologies in all the conditional and unconditional NDC scenarios. Carbon prices lie in the range of 3.4–266.2 US$/tCO2eq during 2025–2050 to achieve 100% GHG emission reductions in 2050. Imposition of early stringent mitigation target lowers the carbon prices in the conditional scenarios towards 2050 when compared to the unconditional scenarios. The rapid uptake of CCS, energy efficiency improvements and electrification of the end-use technologies are identified to be the key measures to transform the energy system of Thailand. Key policy insights By 2050, the Thai economy would face a higher fall in both the GDP and household consumption in the unconditional scenarios than those in the conditional scenarios at all levels of GHG emission reduction. Results indicate that early mitigation efforts can be less costly than the delayed ones in the long-term. The cumulative investment needed to achieve decarbonization in Thailand is estimated to exceed 355 billion US$2005 over the period 2010–2050 in the 100% GHG reduction scenarios. The transmission and distribution investments in the power sector need to increase by 30–35% to attain 100% GHG emission reductions during 2010–2050. The trade deficit improves by up to 23–29% in the various GHG mitigation scenarios in 2050.

Future emissions of particles and gases that cause regional air pollution in California under different greenhouse gas mitigation strategies

Mitigating future climate change and managing future air quality are inter-related fields that have the potential to benefit from coordinated strategies that leverage the efforts in one area to achieve positive outcomes in the other area. California plans to reduce greenhouse gas (GHG) emission by 80% (relative to year 1990) by the year 2050. The changes required to meet this target also have the potential to improve air quality. Previous work developed an energy-economic model CA-TIMES and an emission inventory model CA-REMARQUE to study the possible pathways of meeting the GHG mitigation target and the air pollutant emissions associated with those pathways. Here we update the CA-TIMES and CA-REMARQUE model framework and analyze six different scenarios: (i) BAU - a business-as-usual future reference scenario, (ii) CAP30 - a loose GHG reduction scenario that meets current policy references but only achieves a 40% GHG reduction (relative to 1990 levels) by the year 2030, (iii) GHGAi - a climate-friendly 80% GHG reduction scenario featuring broad adoption of advanced technologies and renewable energies, (iv) CCS - a scenario that achieves 80% net GHG reductions but allows for more fossil energy combustion by focusing on adoption of carbon capture and sequestration technology, (v) NGB– a variation on the GHGAi scenario that allows for more natural gas combustion for residential and commercial buildings, and (vi) NGT – a variation of the GHGAi scenario that allows for more natural gas combustion for electricity generation. Results show that the GHGAi deep GHG mitigation scenario significantly reduces emissions (−41% PM0.1, −8% PM2.5, and −26% NOX) and improves air quality (−1 μg m−3 PM2.5) yielding public health benefits (+USD 20B yr−1) relative to the BAU scenario. The CCS scenario achieves the same GHG reductions but increases emissions in some areas (+2.5% PM2.5) resulting in only one third of the public health benefits compared to the GHGAi scenario. The NGB and NGT scenarios show that an 18% increase in natural gas utilization in buildings or a 15% increase in natural gas power generation offsets 32% and 46% of the ultrafine particle emission reduction achieved in the GHGAi scenario but has little impact on PM2.5 concentrations, producing approximately 90% of the public health benefits of the GHGAi scenario. These public health benefits should be considered when making decisions about future GHG mitigation strategies.

Land transport greenhouse gas mitigation scenarios for Mauritius based on modelling transport demand

A model has been applied to quantify land transport demand in Mauritius from the perspectives of both passenger transport and freight transport. The first objective of the paper is to present the model, including the 2-layer calibration that has been carried out for assuring energy balance using official statistics. The second objective of the paper is to demonstrate the use of the model to develop greenhouse gas mitigation scenarios based on policies including: (i) improved fuel efficiency of vehicles; (ii) improved vehicle inspections; (iii) the scaling up of low-carbon technologies including hybrid and electric vehicles, as well as the use of bioethanol produced in Mauritius; and (iv) the proposed light-rail system in Mauritius. The results show that the ‘business-as-usual’ land transport emissions are expected to reach ~1.68 million tonnes of carbon dioxide equivalent (MtCO2e) in 2050. The combination of all scenarios could result in ~0.31 MtCO2e reductions by 2050, with over 57% of all reductions are expected to accrue from (iii). Since energy used in transport makes up for more than 56% of the primary energy requirement of Mauritius, the macroeconomic effects of decarbonizing land transport is significant from an energy security standpoint. The modelling approach can be used to inform the formulation of a low-carbon land transport strategy for Mauritius, as well as for increasing the mitigation ambition of its Nationally Determined Contribution. Further, it is argued that the approach would be useful in the context of other small islands developing states that face land availability constraints like Mauritius.

Exploring greenhouse gas mitigation strategies for agriculture in Africa: The case of Nigeria.

We used the Agriculture and Land Use National Greenhouse Gas Inventory Software to estimate the total greenhouse gas (GHG) emissions from the Nigerian agriculture sector in 2010. We went ahead to project future GHG emissions up to 2050. Two alternative GHG mitigation scenarios such as moderate (MS) and aggressive (AS) scenarios were developed and examined. Our results showed that total GHG emissions from Nigerian agriculture in 2010 were around 34.9million tonnes of carbon dioxide equivalent. GHG emissions from livestock accounted for about 69.2 % of the total emissions, making it the largest source of GHG emissions in the sector. Nigeria's agriculture GHG emissions are expected to increase by 94 % in 2050 relative to 2010 levels. Mitigation strategies in the Nigerian agriculture sector that do not compromise food security are limited. However, with the implementation of different GHG mitigation strategies in the alternative scenarios, emissions are expected to fall by around 13.2 % and 26.7 % by 2050 in the MS and AS, respectively, compared to the baseline scenario. While the mitigation potentials are significant, we argue that robust and dedicated policies are required to accelerate climate-smart agriculture in Nigeria.

Natural Variability in Projections of Climate Change Impacts on Fine Particulate Matter Pollution

AbstractVariations in meteorology associated with climate change can impact fine particulate matter (PM2.5) pollution by affecting natural emissions, atmospheric chemistry, and pollutant transport. However, substantial discrepancies exist among model‐based projections of PM2.5 impacts driven by anthropogenic climate change. Natural variability can significantly contribute to the uncertainty in these estimates. Using a large ensemble of climate and atmospheric chemistry simulations, we evaluate the influence of natural variability on projections of climate change impacts on PM2.5 pollution in the United States. We find that natural variability in simulated PM2.5 can be comparable or larger than reported estimates of anthropogenic‐induced climate impacts. Relative to mean concentrations, the variability in projected PM2.5 climate impacts can also exceed that of ozone impacts. Based on our projections, we recommend that analyses aiming to isolate the effect climate change on PM2.5 use 10 years or more of modeling to capture the internal variability in air quality and increase confidence that the anthropogenic‐forced effect is differentiated from the noise introduced by natural variability. Projections at a regional scale or under greenhouse gas mitigation scenarios can require additional modeling to attribute impacts to climate change. Adequately considering natural variability can be an important step toward explaining the inconsistencies in estimates of climate‐induced impacts on PM2.5. Improved treatment of natural variability through extended modeling lengths or initial condition ensembles can reduce uncertainty in air quality projections and improve assessments of climate policy risks and benefits.

The impact of different GHG reduction scenarios on the economy and social welfare of Thailand using a computable general equilibrium (CGE) model

BackgroundThe Nationally Determined Contribution (NDC) of Thailand intends to reduce greenhouse gas (GHG) emissions by 20 to 25% from the projected business as usual level by 2030 with the deployment of renewable energy technologies and energy efficiency improvement measures in both the supply and demand sectors. However, in order to contribute towards meeting the long-term goal of the Paris Agreement to stay well below 2 °C, ambitious mitigation efforts beyond 2030 are needed. As such, it is necessary to assess the effects of imposing more stringent long-term GHG reduction targets in Thailand beyond the NDC commitment.MethodsThis paper analyses the macroeconomic effects of limiting the GHG emissions by using a computable general equilibrium (CGE) model on Thailand’s economy during 2010 to 2050. Besides the business as usual (BAU) scenario, this study assesses the macroeconomic effects of ten low to medium GHG mitigation scenarios under varying GHG reduction targets of 20 to 50%. In addition, this study also assesses three different peak emission scenarios, each targeting a GHG reduction of up to 90% by 2050, to analyze the feasibility of zero GHG emissions in Thailand to pursue efforts to hold the global temperature rise to 1.5 °C above pre-industrial levels, as considered in the Paris Agreement.ResultsAccording to the BAU scenario, the GHG emissions from the electricity, industry, and transport sectors would remain the most prominent throughout the planning period. The modeling results indicate that the medium to peak emission reduction scenarios could result in a serious GDP loss compared to the BAU scenario, and therefore, the attainment of such mitigation targets could be very challenging for Thailand. Results suggest that the development and deployment of energy-efficient and renewable energy-based technologies would play a significant role not only in minimizing the GHG emissions but also for overcoming the macroeconomic loss and lowering the price of GHG emissions.ConclusionsThe results reveal that without a transformative change in the economic structure and energy system of Thailand, the country would have to face enormous cost in reducing its GHG emissions.

Assessment of long-term energy efficiency improvement and greenhouse gas emissions mitigation potentials in the chemical sector

The industrial sector accounts for more than one-third of global energy consumption and greenhouse gas (GHG) emissions. In the sector, the chemical industry makes up nearly 30% of global industrial energy consumption. In this study, a framework is developed to apply bottom-up energy modelling for analyzing energy efficiency improvement and GHG mitigation potential in the chemical sector. Also, techno-economic and policy analyses are used to analyze the applicability and economic performance of different mid- to long-term energy efficiency measures. The comprehensive, data-intensive, and technology-rich model is flexible and can be used to study the GHG mitigation potential in different regions. A case study was conducted for Alberta, one of the biggest industrial hubs in Canada, using the Long-range Energy Alternative Planning model. Twenty-eight GHG mitigation scenarios in the petrochemical and fertilizer industries (the two major chemical industry sub-sectors) were developed. The costs of implementing each mitigation measure were calculated and used to develop cost curves. The results suggest that cumulative mitigation potential from the chemical industry would be 7.1 and 29.7 megatonnes CO2 equivalent in the periods ending 2030 and 2050, respectively. We also found that more than 75% of the emissions reductions are achievable with negative cost.

Economics of mitigating greenhouse gas emissions from beef production in western Canada

Beef production plays a vital role in the economy of western Canada; however, in the wake of global warming as a result of increasing greenhouse gas (GHG) emissions, the industry has come under some scrutiny. Although there has been encouraging scientific findings on mitigation strategies applicable to beef operations, there is a lack of economic analysis of such strategies. This study extends on the work of Beauchemin et al. (2011) and evaluates the economic impacts of greenhouse gas mitigation scenarios (GHGMS) for beef operations, and in the process identifies economic and environmental sustainable scenarios. A whole farm economic simulation model was developed and used to measure the profitability of eleven GHGMS adopted from Beauchemin et al. (2011). Whole farm present value gross margin of the eleven scenarios was measured and compared to the conversional system (baseline) of a farm in Vulcan County, Southern Alberta. The farm had 120 cows and their progeny, which was raised and finished on the farm for sale. The study farm was simulated over a period of 8 years in order to fully account for the lifetime economic activity of the breeding stock, as well as the progeny raised for sale. Simulation results estimated a whole farm present value gross margin per ha of $3.51 for the baseline. Seven of the eleven scenarios evaluated were found to increase profitability of the farm by up to 4%. However, only six of the scenarios were found to be both economically and environmentally sustainable to the farm. Four of the six sustainable scenarios were strategies applied to the breeding stock and two to the feedlot. These results suggest that beef producers can profitably implement several GHG mitigation strategies to their operations without substantial changing their operational system.

Development of a Framework for the Assessment of Energy Demand-Based Greenhouse Gas Mitigation Options for the Agriculture Sector

Abstract. This study assesses greenhouse gas (GHG) mitigation options for the agriculture sector. The Long-range Energy Alternatives Planning (LEAP) model was used to develop a framework to assess future trends in energy demand and associated GHG emissions for the agriculture sector and to assess various GHG mitigation options associated with energy consumption. A business-as-usual (reference) scenario and 32 GHG mitigation scenarios were developed for the years 2009-2050 using the LEAP model. A case study for Alberta, Canada, was conducted. In the model, GHG mitigation scenarios were developed for the energy demand side (e.g., farm machines, farm transportation, lighting, and ventilation) based on efficiency improvements and the use of renewable energy. The mitigation scenarios were divided into two planning horizons based on technology penetration: slow penetration (2009-2050) and fast penetration (2009-2030). For each planning horizon, 16 scenarios were assessed. Of all farm machines, efficient diesel tractors have the highest GHG mitigation potential: 12.35 MT of CO2 equivalent by 2050 and 4.7 MT of CO2 equivalent by 2030. In addition, GHG abatement cost curves show that biodiesel tractors and efficient diesel tractors have the highest GHG mitigation potential, with attractive abatement costs of -$62 and -$11 tonne-1 of CO2 mitigated by 2050, respectively. Keywords: Abatement cost, Agriculture sector, Energy efficiency, GHG mitigation, LEAP model.

Cobenefits of global and domestic greenhouse gas emissions for air quality and human health

BackgroundReductions in greenhouse gas emissions often reduce emissions of coemitted air pollutants, yielding cobenefits for air quality and human health. Here, we report results of a global cobenefits study—the first to use a global atmospheric model and consistent future scenarios—and results from follow-on studies that downscale those global results to focus on the continental US. MethodsWe use the RCP4.5 scenario as an aggressive global greenhouse gas mitigation scenario, and compare it with its associated reference case, the difference between these scenarios is uniquely attributable to the global carbon policy. FindingsIn the global study, we find that global greenhouse gas mitigation avoids roughly 0·5 million air pollution-related deaths per year in 2030, 1·3 million air pollution-related deaths per year in 2050, and 2·2 million air pollution-related deaths per year in 2100. Global average cobenefits are US$50–380 per ton of CO2 reduced, which exceeds previous estimates. These cobenefits also exceed the marginal abatement costs in 2030 and 2050. Cobenefits here are higher than in previous studies because we account for global air pollution transport, and because of projected population, and baseline mortality growth. We then downscale these results in 2050 to the continental USA to project these cobenefits at fine resolution, using the WRF, SMOKE and CMAQ models, and we separate the contributions of domestic and foreign reductions to US cobenefits. We find that for PM2.5, most of the air quality and health cobenefits are from domestic emissions. By contrast, for ozone, most of the cobenefits results from foreign emissions, including global methane reductions. InterpretationThese results suggest that the air quality and health cobenefits realised by one country will be much greater if foreign countries also reduce greenhouse gas emissions in a coordinated effort. We also conclude that previous studies that focus on domestic or local cobenefits might significantly underestimate the total cobenefits of global greenhouse gas reductions. FundingUS Environmental Protection Agency, the Integrated Assessment Research Program in the US Department of Energy, Office of Science, the National Institute of Environmental Health Sciences, the Portuguese Foundation for Science and Technology, and an EPA STAR Graduate Fellowship.

Assessing climate change impacts, benefits of mitigation, and uncertainties on major global forest regions under multiple socioeconomic and emissions scenarios

We analyze a set of simulations to assess the impact of climate change on global forests where MC2 dynamic global vegetation model (DGVM) was run with climate simulations from the MIT Integrated Global System Model-Community Atmosphere Model (IGSM-CAM) modeling framework. The core study relies on an ensemble of climate simulations under two emissions scenarios: a business-as-usual reference scenario (REF) analogous to the IPCC RCP8.5 scenario, and a greenhouse gas mitigation scenario, called POL3.7, which is in between the IPCC RCP2.6 and RCP4.5 scenarios, and is consistent with a 2 °C global mean warming from pre-industrial by 2100. Evaluating the outcomes of both climate change scenarios in the MC2 model shows that the carbon stocks of most forests around the world increased, with the greatest gains in tropical forest regions. Temperate forest regions are projected to see strong increases in productivity offset by carbon loss to fire. The greatest cost of mitigation in terms of effects on forest carbon stocks are projected to be borne by regions in the southern hemisphere. We compare three sources of uncertainty in climate change impacts on the world’s forests: emissions scenarios, the global system climate response (i.e. climate sensitivity), and natural variability. The role of natural variability on changes in forest carbon and net primary productivity (NPP) is small, but it is substantial for impacts of wildfire. Forest productivity under the REF scenario benefits substantially from the CO2 fertilization effect and that higher warming alone does not necessarily increase global forest carbon levels. Our analysis underlines why using an ensemble of climate simulations is necessary to derive robust estimates of the benefits of greenhouse gas mitigation. It also demonstrates that constraining estimates of climate sensitivity and advancing our understanding of CO2 fertilization effects may considerably reduce the range of projections.

Distributed Cogeneration of Power and Heat within an Energy Management Strategy for Mitigating Fossil Fuel Consumption

SummaryDistributed energy sources, such as self‐power generation, steam boilers, and combined heat and power production (CHP), are operated to manage the supply of energy by optimizing the costs of meeting the demand for electricity and heat. This article was written in conjunction with reports by the United Nations Environment Program's International Resource Panel that quantifies and compares the environmental and natural resource impacts and benefits of using demand‐side efficient technologies for greenhouse gas mitigation scenarios from now until 2050. In this article, we examine the potential of using distributed energy sources in future energy systems. First, we reviewed the existing research into several energy technologies, especially into cogeneration systems for CHP, using a bibliometric analysis. The current energy supply/demand in the demand‐side sectors in Japan is also reviewed using available statistical data, and an investigation into the energy requirements of industrial manufacturers was performed. After systematizing the results of our review on progress in current research, a scenario analysis was conducted on the potential of distributed energy sources to clarify the contribution of the various technology options. A mismatch between the quality of energy produced, especially heat, or any benefits arising from scale from other energy technologies, can decrease the incentive to implement distributed energy technologies. As a requirement of a regional energy system design and management, distributed energy sources should be considered so that the appropriate technology options can be adopted for the desired energy supply in the demand‐side sector. The possibility exists to replace conventional single‐generation technologies, such as boilers or power generators, with multigeneration technologies. A change in the grid power mix is one of the most sensitive parameters affecting the performance of cogeneration technologies.

Comparison of low-carbon pathways for California

Jurisdictions throughout the world are contemplating greenhouse gas (GHG) mitigation strategies that will enable meeting long-term GHG targets. Many jurisdictions are now focusing on the 2020–2050 timeframe. We conduct an inter-model comparison of nine California statewide energy models with GHG mitigation scenarios to 2050 to better understand common insights across models, ranges of intermediate GHG targets (i.e., for 2030), necessary technology deployment rates, and future modeling needs for the state. The models are diverse in their representation of the California economy: across scenarios with deep reductions in GHGs, annual statewide GHG emissions are 8–46 % lower than 1990 levels by 2030 and 59–84 % lower by 2050 (not including the Wind-Water-Solar model); the largest cumulative reductions occur in scenarios that favor early mitigation; non-hydroelectric renewables account for 30–58 % of electricity generated for the state in 2030 and 30–89 % by 2050 (not including the Wind-Water-Solar model) ; the transportation sector is decarbonized using a mix of energy efficiency gains and alternative-fueled vehicles; and bioenergy is directed almost exclusively towards the transportation sector, accounting for a maximum of 40 % of transportation energy by 2050. Models suggest that without new policies, emissions from non-energy sectors and from high-global-warming-potential gases may alone exceed California’s 2050 GHG goal. Finally, future modeling efforts should focus on the: economic impacts and logistical feasibility of given scenarios, interactive effects between two or more climate policies, role of uncertainty in the state’s long-term energy planning, and identification of pathways that achieve the dual goals of criteria pollutant and GHG emission reduction.

An uncertainty approach to assessment of climate change impacts on the Zambezi River Basin

Many residents of the Zambezi River Valley are dependent on water-related resources. Greenhouse gas (GHG) emissions may cause a significant change to the climate in the Zambezi Basin in the future, but there is much uncertainty about the future climate state. This situation leaves policy makers at a state of urgency to prepare for these changes as well as reduce the impacts of the changes through GHG mitigation strategies. First and foremost, we must better understand the economic sectors most likely impacted and the magnitude of those impacts, given the inherent uncertainty. In this study, we present a suite of models that assess the effects of climate change on water resources for four countries in the Zambezi basin: Malawi, Mozambique, Zambia, and Zimbabwe. We use information from a large ensemble (6800) of climate scenarios for two GHG emission policies which represent a distribution of impacts on water-related sectors, considering emissions uncertainty, climate sensitivity uncertainty, and regional climate uncertainty. Two GHG mitigation scenarios are used to understand the effect of global emissions reduction on the River Basin system out to 2050. Under both climate polices, the majority of the basin will likely be drier, except for a portion in the north around Malawi and northern Zambia. Three Key Performance Indicators are used—flood occurrence, unmet irrigation demand, and hydropower generation—to understand the impact channels of climate change effects on the four countries. We find that floods are likely to be worse in Mozambique, irrigation demands are likely to be unmet in Mozambique and Zimbabwe, and hydropower generation is likely to be reduced in Zambia. We also find that the range of possible impacts is much larger under an unconstrained GHG emissions case than under a strict mitigation strategy, suggesting that GHG mitigation would reduce uncertainties about the future climate state, reducing the risks of extreme changes as compared to the unconstrained emissions case.

Costs and benefits of differences in the timing of greenhouse gas emission reductions

Most modelling studies that explore long-term greenhouse gas mitigation scenarios focus on cost-efficient emission pathways towards a certain climate target, like the internationally agreed target to keep global temperature increase below 2 °C compared to pre-industrial levels (the 2 °C climate target). However, different timing of reductions lead to different transient temperature increase over the course of the century and subsequently to differences in the time profiles of not only the mitigation costs but also adaptation costs and residual climate change damage. This study adds to the existing literature by focussing on the implication of these differences for the evaluation of a set of three mitigation scenarios (early action, gradual action and delayed action), all three limiting global temperature increase below 2 °C above pre-industrial levels, using different discount rates. The study shows that the gradual mitigation pathway is, for these discount rates, preferred over early or delayed action in terms of total climate costs and net benefits. The relative costs and benefits of the early or delayed mitigation action scenarios, in contrast, do strongly depend on the discount rate applied. For specific discount rates, these pathways might therefore be preferred for other reasons, such as reducing long-term uncertainty in climate costs by early action.

Linkages between the energy and agricultural sectors: insights from European Union greenhouse gas mitigation scenarios

Over the past decade, the relationship between agricultural and energy markets has strengthened. Traditional energy sources have been increasingly replaced by energy from biomass, and this trend is expected to continue into the future. Consequently, an assessment of the efficiency of bioenergy policies requires a comprehensive analysis of both agricultural and energy markets. The objective of this paper is to analyze the impacts of two detailed European Union (EU) greenhouse gas (GHG) emission mitigation policies on the utilization of biomass for energy production and the implications for agricultural prices and trade. The consequences of a policy-induced shift from consumption of fossil to renewable energy are assessed under full consideration of interrelations between the energy and agricultural sectors. To this end, we combine an energy system model and an agricultural sector model by establishing a consistent interface between them. Depending on the ambition of the GHG emission reduction scenarios, the results indicate significant price increases. Furthermore, the increase in European demand for energy crops is to a substantial degree covered by additional imports. These results highlight that GHG emission mitigation policies enacted in a large economy like the EU cannot be considered without accounting for indirect effects in the rest of the world. They put the efficiency and also the effectiveness of such policies in general into question.

Regional Grain Yield Response to Climate Change in China: A Statistic Modeling Approach

China is the world’s most populous country with only 7% of the world’s arable land. Accurate assessment of the effect that future climate change may pose on grain production is essential to the sustainability of agriculture. Model variations plus uncertainties in the future climate change scenarios create a big challenge for such evaluation. In this work, we developed the statistical models for six different regions in China, using the historical yield data between 1981 and 2010 from the National Bureau of Statistics combined with meteorological station observations and analyzed the impact of climate variation (i.e., temperature and precipitation changes) on the grain yields into the 2030s, based on 28 ensemble climate predictions from six state-of-the-art Coupled Model Intercomparison Project Phase 5 (CMIP5) model outputs. Our results indicate that the four crops (i.e., rice, maize, wheat, and soybean) respond similarly to the climate variation in different regions of China, with the sensitivity to warming increasing from north to south and from inner land to coast regions. In addition, the yields of all the four crops in East and Central-South China are also positively correlated with precipitation change. Future projections with a medium greenhouse gas mitigation scenario (RCP4.5) showed that the yield of the four crops in six regions of China would increase ranging from 0.02 to 1.19 hundred ton/ha, in 2030s with respect to the 2000s. Nevertheless, adaptive implementations such as appropriately improve the irrigation infrastructure in East and Central-South China could mitigate the adverse impact from future climate change.

Implications of delayed participation and technology failure for the feasibility, costs, and likelihood of staying below temperature targets—Greenhouse gas mitigation scenarios for the 21st century

The feasibility of limiting greenhouse gas concentrations and associated global mean temperature increase to 2 °C above preindustrial levels has recently attracted considerable scientific and policy attention. Whether or not such low targets can be achieved in the long-term depends on a number of assumptions about, for instance, technological change and the willingness of countries to immediately join a post-Kyoto agreement to limit anthropogenic climate change. As part of the EMF22 international scenarios study, our paper contributes to the ongoing discussion by conducting a scenario analysis in order to examine the influence of these assumptions on the feasibility of a set of radiative forcing targets. We distinguish between “first-best scenarios” with full participation, and “second-best scenarios” with delayed participation of Brazil, China, India, and Russia in 2030 and the rest of Non-Annex I countries after 2050. In addition, we explored the robustness of our “first-best” scenario results by varying the assumptions with regard to availability of key technology clusters. Accounting for the deep uncertainties associated with future global mean temperature change, we conduct a probabilistic assessment and explore the scenario's likelihood for staying below a range of different temperature thresholds. Our results indicate the need for a diverse portfolio of mitigation technologies in order to decarbonize the energy system. Efficiency and enhanced energy conservation play an important role across all scenarios. In addition, carbon capture and storage (CCS) is found to be important for achieving particularly low forcing targets as well as for avoiding pronounced overshoot of the target in the medium-term. Negative emission technologies such as bioenergy with CCS have a strong influence on the timing of mitigation; i.e. without their availability much more early action is needed. According to our analysis, significant delay in the participation of major emitters leads to the unattainability of the most stringent forcing targets. Moreover, delayed participation may lead to excessive additional global mitigation cost that may even exceed the mitigation costs of the most stringent forcing targets in the best case with full participation. We find that for countries that significantly delay their participation, the early gains from postponing mitigation may be offset to a large extent in the long-term, due to the need for more pronounced mitigation later in the century. Linking the radiative forcing targets with temperature change, we find that the chances of staying below certain temperature thresholds may be significantly lower over the course of the century than would be by looking exclusively at 2100. From a policy perspective our results therefore suggest that focusing on early abatement to avoid significant overshoot of concentrations may be as important as the long-term concentration target itself.

ECLIPSE: An integrated energy-economy model for climate policy and scenario analysis

Abstract This paper describes the development of the Energy and Climate Policy and Scenario Evaluation (ECLIPSE) model—a flexible integrated assessment tool for energy and climate change policy and scenario assessment. This tool builds on earlier efforts to link top-down and bottom-up models, and combines a macroeconomic energy demand model and a consumer-budget transport demand model with the technology-rich bottom-up energy and transport system model Energy Research and Investment Strategy (ERIS), and solves the models iteratively. Compared to previous efforts, ECLIPSE includes many new features, such as a more disaggregated production function, improved calibration and parameterization and separate modeling of passenger transport demand. The separate modeling of transportation makes ECLIPSE particularly well-suited to analyzing interactions between the transport sector and the broader energy market and economy. This paper presents results illustrating some features of the integrated model, compares technology deployment results with ECLIPSE and the bottom-up ERIS model, and briefly describes illustrative baseline and greenhouse gas mitigation scenarios to highlight some of the features of the framework outlined in this paper. A number of modeling and policy insights arising from this scenario analysis are discussed.