Wildfire-induced reduction in the carbon storage of Mediterranean ecosystems: An application to brush and forest fires impacts assessment

Wildfire is one of the most dangerous and harmful phenomena in the world. Hence, fire impacts assessment could become very important in forest areas according to its environmental and landscape values. This paper suggests an approach to identify fire effects on biomass, in consonance with the potential carbon storage of each area used, and its biomass consumption based on fire behavior.Dense mature forests were the most vulnerable landscapes based on its aboveground biomass, mainly tree stem biomass. A significant correlation was found between fire intensity and biomass consumption. Biomass consumption ranged from 16.59% to 98.75% from the two studied wildfires. It is necessary to provide a scenario analysis according to the uncertain CO₂ market. As an example, carbon storage impacts in one fire were between 100,340.66 € (using the minimum price of CO₂) and 741,057.44 € (using the maximum price of CO₂). Differences between scenarios ranged from 35.30% to 46.51% of the total carbon storage impacts. This approach might be a solution to identify and prioritize areas for restoration activities and optimize the allocation of the resources.     

The Impact of Uncertainty in Climate Targets and CO2 Storage Availability on Long-Term Emissions Abatement

A major characteristic of our global interactive climate-energy system is the large uncertainty that exists with respect to both future environmental requirements and the means available for fulfilling these. Potentially, a key technology for leading the transition from the current fossil fuel-dominated energy system to a more sustainable one is carbon dioxide capture and storage. Uncertainties exist, however, concerning the large-scale implementability of this technology, such as related to the regional availability of storage sites for the captured CO₂. We analyze these uncertainties from an integrated assessment perspective by using the bottom-up model TIAM-ECN and by studying a set of scenarios that cover a range of different climate targets and technology futures. Our study consists of two main approaches: (1) a sensitivity analysis through the investigation of a number of scenarios under perfect foresight decision making and (2) a stochastic programming exercise that allows for simultaneously considering a set of potential future states-of-the-world. We find that, if a stringent climate (forcing) target is a possibility, it dominates the solution: if deep CO₂ emission reductions are not started as soon as possible, the target may become unreachable. Attaining a stringent climate target comes in any case at a disproportionally high price, which indicates that adaptation measures or climate damages might be preferable to the high mitigation costs such a target implies.     

CO2 Capture and Storage with Leakage in an Energy-Climate Model

Geological CO₂ capture and storage (CCS) is among the main near-term contenders for addressing the problem of global climate change. Even in a baseline scenario, with no comprehensive international climate policy, a moderate level of CCS technology is expected to be deployed, given the economic benefits associated with enhanced oil and gas recovery. With stringent climate change control, CCS technologies will probably be installed on an industrial scale. Geologically stored CO₂, however, may leak back to the atmosphere, which could render CCS ineffective as climate change reduction option. This article presents a long-term energy scenario study for Europe, in which we assess the significance for climate policy making of leakage of CO₂ artificially stored in underground geological formations. A detailed sensitivity analysis is performed for the CO₂ leakage rate with the bottom-up energy systems model MARKAL, enriched for this purpose with a large set of CO₂ capture technologies (in the power sector, industry, and for the production of hydrogen) and storage options (among which enhanced oil and gas recovery, enhanced coal bed methane recovery, depleted fossil fuel fields, and aquifers). Through a series of model runs, we confirm that a leakage rate of 0.1%/year seems acceptable for CCS to constitute a meaningful climate change mitigation option, whereas one of 1%/year is not. CCS is essentially no option to achieve CO₂ emission reductions when the leakage rate is as high as 1%/year, so more reductions need to be achieved through the use of renewables or nuclear power, or in sectors like industry and transport. We calculate that under strict climate control policy, the cumulative captured and geologically stored CO₂ by 2100 in the electricity sector, when the leakage rate is 0.1%/year, amounts to about 45,000 MtCO₂. Only a little over 10,000 MtCO₂ cumulative power-generation-related emissions are captured and stored underground by the end of the century when the leakage rate is 1%/year. Overall marginal CO₂ abatement costs increase from a few €/tCO₂ today to well over 150 €/tCO₂ in 2100, under an atmospheric CO₂ concentration constraint of 550 ppmv. Carbon costs in 2100 turn out to be about 40 €/tCO₂ higher when the annual leakage rate is 1%/year in comparison to when there is no CO₂ leakage. Irrespective of whether CCS deployment is affected by gradual CO₂ seepage, the annual welfare loss in Europe induced by the implementation of policies preventing “dangerous anthropogenic interference with the climate system” (under our assumption, implying a climate stabilisation target of 550 ppmv CO₂ concentration) remains below 0.5% of GDP during the entire century.

The Kyoto Protocol and non-CO2 Greenhouse Gases and Carbon Sinks

Many trace constituents other than carbon dioxide affect the radiative budget of the atmosphere. The existing international agreement to limit greenhouse gases, the Kyoto Protocol, includes carbon dioxide (CO₂), methane (CH₄), nitrous oxide (N₂O), hydrofluorocarbons (HFCs), perfluorocarbons (PFCs), sulfur hexafluoride (SF₆) and credit for some carbon sinks. We investigate technological options for reducing emissions of these gases and the economic implications of including other greenhouse gases and sinks in the climate change control policy. We conduct an integreated assessment of costs using the MIT Emissions Prediction and Policy Analysis (EPPA) model combined with estimates of abatement costs for non-CO₂ greenhouse gases and sinks. We find that failure to take advantage of the other gas and sink flexibility would nearly double aggregate Annex B costs. Including all the GHGs and sinks is actually cheaper than if only CO₂ had been included in the Protocol and their inclusion achieves greater overall abatement. There remains considerable uncertainty in these estimates, the magnitude of the savings depends heavily on reference projections of emissions, for example, but these uncertainties do not change the overall conclusion that non-CO₂ GHGs are an important part of a climate control policy.     

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.     

Nuclear Versus Coal plus CCS: a Comparison of Two Competitive Base-Load Climate Control Options

In this paper, we analyze the relative importance and mutual behavior of two competing base-load electricity generation options that each are capable of contributing significantly to the abatement of global CO₂ emissions: nuclear energy and coal-based power production complemented with CO₂ capture and storage (CCS). We also investigate how, in scenarios developed with an integrated assessment model that simulates the economics of a climate-constrained world, the prospects for nuclear energy would change if exogenous limitations on the spread of nuclear technology were relaxed. Using the climate change economics model World Induced Technical Change Hybrid, we find that until 2050 the growth rates of nuclear electricity generation capacity would become comparable to historical rates observed during the 1980s. Given that nuclear energy continues to face serious challenges and contention, we inspect how extensive the improvements of coal-based power equipped with CCS technology would need to be if our economic optimization model is to significantly scale down the construction of new nuclear power plants.     

Modelling and assessing inter-regional trade of CO2 emission reduction units

A cost-efficient way to allocate carbon dioxide (CO₂) emission reductions among countries or regions is to harmonise their marginal reduction costs. This could be achieved by a market of emission reduction units (ERUs). To model such a market, we use a multi-regional MARKAL-MACRO model. It gives insights into the consequences of co-ordinating CO₂ abatement on regional energy systems and economies. As a numerical application, we assess the establishment of a market of ERUs among three European countries for curbing their CO₂ emissions.     

Non-linear Pass-Through of the CO2 Emission-Allowance Price onto Wholesale Electricity Prices

This article considers the evidence for threshold effects in the relationship between electricity and emission-permit prices in France and Germany during the second phase of the EU ETS. Specifically, we compare linear and non-linear threshold models of electricity prices using the sample-splitting and threshold estimation approach in Hansen (Econometrica, 64 575–603 2000). We find evidence of non-linear threshold effects in both countries. The estimated carbon-price thresholds are 14.94€ and 12.57€ in France and Germany respectively. The carbon-price threshold in France perfectly matches the well-known carbon spot-price structural break of October 2008. This is not the case for the carbon-price threshold in Germany. Further analysis reveals that carbon prices before October 2008 were not reflected in electricity prices in either country. This is mainly due to uncertainty about the future of the EU ETS that led electricity producers to adopt a wait-and-see attitude. After October 2008, French electricity producers passed the price of emission permits through to electricity prices in a linear way, while their German counterparts did so non-linearly. Finally, we suggest improvements to the design of the EU ETS. Our recommendations are to strengthen the price signal to make it more clear and reliable and provide sufficient incentives for energy transition.     

Development of environmental impact monitoring protocol for offshore carbon capture and storage (CCS): A biological perspective

Offshore geologic storage of carbon dioxide (CO₂), known as offshore carbon capture and sequestration (CCS), has been under active investigation as a safe, effective mitigation option for reducing CO₂ levels from anthropogenic fossil fuel burning and climate change. Along with increasing trends in implementation plans and related logistics on offshore CCS, thorough risk assessment (i.e. environmental impact monitoring) needs to be conducted to evaluate potential risks, such as CO₂ gas leakage at injection sites. Gas leaks from offshore CCS may affect the physiology of marine organisms and disrupt certain ecosystem functions, thereby posing an environmental risk. Here, we synthesize current knowledge on environmental impact monitoring of offshore CCS with an emphasis on biological aspects and provide suggestions for better practice. Based on our critical review of preexisting literatures, this paper: 1) discusses key variables sensitive to or indicative of gas leakage by summarizing physico-chemical and ecological variables measured from previous monitoring cruises on offshore CCS; 2) lists ecosystem and organism responses to a similar environmental condition to CO₂ leakage and associated impacts, such as ocean acidification and hypercapnia, to predict how they serve as responsive indicators of short- and long-term gas exposure, and 3) discusses the designs of the artificial gas release experiments in fields and the best model simulation to produce realistic leakage scenarios in marine ecosystems. Based on our analysis, we suggest that proper incorporation of biological aspects will provide successful and robust long-term monitoring strategies with earlier detection of gas leakage, thus reducing the risks associated with offshore CCS.     

Marginal Abatement Cost Curves: Combining Energy System Modelling and Decomposition Analysis

Marginal abatement cost (MAC) curves are a useful policy tool to communicate findings on the technological structure and the economics of CO₂ emissions reduction. However, existing ways of generating MAC curves do not display consistent technological detail and do not consider system-wide interactions and uncertainty in a structured manner. This paper details a new approach to overcome the present shortcomings by using an energy system model, UK MARKAL, in combination with index decomposition analysis. In addition, this approach allows different forms of uncertainty analysis to be used in order to test the robustness of the MAC curve. For illustration purposes, a sensitivity analysis concerning fossil fuel prices is applied to the transport sector of the UK. The resulting MAC curves are found to be relatively robust to different fuel costs at higher CO₂ tax levels. The new systems-based approach improves MAC curves through the avoidance of an inconsistent emissions baseline, the incorporation of system-wide interactions and the price responsiveness of demand.     

The impact of terrorism and FDI on environmental pollution: Evidence from Afghanistan,Iraq, Nigeria,Pakistan, Philippines,Syria, Somalia,Thailand and Yemen

In this study, the relationships among environmental pollution, terrorism, foreign direct investments (FDI), energy consumption and economic growth is investigated for Afghanistan, Iraq, Nigeria, Pakistan, Philippines, Syria, Somalia, Thailand and Yemen covering the 1975–2017 period utilizing Panel cointegration tests, ANOVA tests, long-run estimators and panel trivariate Causality tests. ANOVA results are in favor of evidence of homogeneity between the selected countries. Long-run estimators reveal that terrorism, FDI, energy consumption and economic growth have statistically significant effects on environmental pollution. Panel trivariate Causality test determines the causal relationship between the variables. Accordingly, one-way causal nexus from terrorism to Carbon dioxide (CO₂) emissions and from FDI inflows to CO₂ emissions are found in the short-run. In the long-run, with strong causality results, the evidence of bi-directional causality between CO₂ emissions and other variables, namely, terrorism, FDI inflow energy consumption and economic growth are detected.     

Influence of elevated carbon dioxide and temperature on belowground carbon allocation and enzyme activities in tropical flooded soil planted with rice

Changes in the soil labile carbon fractions and soil biochemical properties to elevated carbon dioxide (CO₂) and temperature reflect the changes in the functional capacity of soil ecosystems. The belowground root system and root-derived carbon products are the key factors for the rhizospheric carbon dynamics under elevated CO₂ condition. However, the relationship between interactive effects of elevated CO₂ and temperature on belowground soil carbon accrual is not very clear. To address this issue, a field experiment was laid out to study the changes of carbon allocation in tropical rice soil (Aeric Endoaquept) under elevated CO₂ and elevated CO₂ + elevated temperature conditions in open top chambers (OTCs). There were significant increase of root biomass by 39 and 44 % under elevated CO₂ and elevated CO₂ + temperature compared to ambient condition, respectively. A significant increase (55 %) of total organic carbon in the root exudates under elevated CO₂ + temperature was noticed. Carbon dioxide enrichment associated with elevated temperature significantly increased soil labile carbon, microbial biomass carbon, and activities of carbon-transforming enzyme like β-glucosidase. Highly significant correlations were noticed among the different soil enzymes and soil labile carbon fractions.     

An Integrated Assessment by Models for Energy Systems Analysis and Life-Cycle Assessment with a Case Study of Advanced Fossil-Fired Power Plants in China

We developed an integrated assessment (IA) using models for energy systems analysis and life-cycle assessment (LCA). Based on this assessment framework, we developed cost-benefit analysis (CBA) case studies for a hypothetical project designed to introduce advanced fossil-fired power generation technologies in China. Our MARKAL model for Japan confirmed that radical reductions (i.e., 80 % by 2050) of carbon dioxide (CO₂) could be attained from energy systems alone and that credit for emission allowances was required. We evaluated life-cycle costs and emissions of carbon dioxide, sulfur oxide, and nitrogen oxide gases for the energy technologies using an LCA model. Further, we applied a power generation planning model for six Chinese grids to provide a power mix structure, potentially producing credit by installing fossil-fired power generation technology and by using baseline grid emission factors with an average cost of electricity. Finally, by using dynamic emission reductions and additional costs from the two models, we conducted case studies of CBA for a hypothetical project to install the technologies in China. This was accomplished by evaluating emission reductions in monetary terms and by applying a life-cycle impact assessment model. A unique feature of our IA is its dynamic (time-varying) assessment of costs and benefits.     

Carbon fixation efficiency of plants influenced by sulfur dioxide

In the land ecosystem, the forest can absorb the carbon dioxide (CO₂) in the atmosphere and turn the CO₂ into organic carbon to store it in the plant body. About 2 × 10¹¹ tons of CO₂ changes through photosynthesis into organic matter by plant annually. In this research, ten kinds of woody plants were selected for assessing the carbon fixation ability influenced by sulfur dioxide (SO₂). The tested trees were put into a fumigation chamber for 210 days in a 40-ppb SO₂ environment. The results of this study showed that there was no clear symptom of tested trees under a 40-ppb SO₂ environment. The tested trees could tolerate this polluted environment, but it will impact their CO₂ absorption ability. The carbon fixation ability will reduce as the polluted period lengthens. The carbon fixation potential of tested trees ranged from 2.1 to 15.5 g·CO₂/m²·d with an average of 7.7 g·CO₂/m²·d. The changes in CO₂ absorption volume for Messerschmidia argentea were more stable during the fumigation period with a variation of 102%. Among the tested trees, Diospyros morrisiana had the best carbon fixation potential of 9.19 g·CO₂/m²·d and M. argentea had the least with 2.54 g·CO₂/m²·d.     

Marginal Abatement Costs and Marginal Welfare Costs for Greenhouse Gas Emissions Reductions: Results from the EPPA Model

Marginal abatement cost (MAC) curves, relationships between tonnes of emissions abated and the CO₂ (or greenhouse gas (GHG)) price, have been widely used as pedagogic devices to illustrate simple economic concepts such as the benefits of emissions trading. They have also been used to produce reduced-form models to examine situations where solving the more complex model underlying the MAC is difficult. Some important issues arise in such applications: (1) Are MAC relationships independent of what happens in other regions?, (2) are MACs stable through time regardless of what policies have been implemented in the past?, and (3) can one approximate welfare costs from MACs? This paper explores the basic characteristics of MAC and marginal welfare cost (MWC) curves, deriving them using the MIT Emissions Prediction and Policy Analysis model. We find that, depending on the method used to construct them, MACs are affected by policies abroad. They are also dependent on policies in place in the past and depend on whether they are CO₂-only or include all GHGs. Further, we find that MACs are, in general, not closely related to MWCs and therefore should not be used to derive estimates of welfare change. We also show that, as commonly constructed, MACs may be unreliable in replicating results of the parent model when used to simulate GHG policies. This is especially true if the policy simulations differ from the conditions under which the MACs were simulated.     

A methodology to estimate carbon storage and flux in forestland using existing forest and soils databases

Sequestration of carbon through expansion and management offorestland can assist in reducing greenhouse gas concentrationsin the atmosphere. Quantification of the amount of carbonpresents an ongoing challenge that calls for new approaches.These new approaches must seek to simplify the science-basedaccounting of carbon storage and flux, while adhering to generalprinciples of greenhous gas accounting. Quantifying change incarbon storage and carbon flux consists of two steps: developinga baseline of carbon storage, and measuring resulting storageand flux following a change of conditions. A methodology isproposed that accomplishes both steps, applicable to anaggregate-level analysis using the state of Iowa (U.S.A.) as a case study. The method combines existing databasesfrom the U.S. Forest Service (USFS) and U.S. Department ofAgriculture (USDA), and merges these with the methods of Birdsey(USDA, 1992, 1995; IPCC, 1997; EIIP, 1999) for partitioningcarbon stocks into storage pools. Forested ecosystems in the study area contain approximately 137.3 metric tons organiccarbon per hectare, or 114 million metric tons of carbon inaggregate. Of this total, 44.7 million tons are stored inbiomass tissue, and 69.2 million tons of carbon are contained insoils. Carbon flux due to forests in the state of Iowa isestimated to be a net annual sequestration (removal from theatmosphere) of 4.3 million metric tons of CO₂-equivalent,approximately 5% of the net annual CO₂-equivalentemissions from the state (Ney et al., 1996).     

Greenhouse gas emissions from forestry,land-use changes,and agriculture in Tanzania

The purpose of the study was to identify and quantify anthropogenic sources and sinks of greenhouse gases from forestry, land-use changes and agriculture in Tanzania. The 1990 inventory revealed that, in the land-use sector, methane (CH₄) and carbon dioxide (CO₂) are the primary gases emitted. Enteric fermentation in livestock production systems is the largest source of CH₄. Although deforestation results in greenhouse gas emissions, the managed forests of Tanzania are a major CO₂ sink.     

On a Holistic Modeling Approach for Managing Carbon Emission Ecosystems

Effective use of historical volumes of heterogeneous and multidimensional data is a major challenge, especially projects associated with potential applications of carbon emission ecosystems. Data science in these applications becomes tedious when such varied data are accumulated and or distributed in multiple domains. Design, development, and implementation of sustainable geological storages are crucial for managing carbon dioxide (CO₂) emissions and its modeling process. The purpose of the research is to address major challenges and how best a robust “ontology-based multidimensional data warehousing and mining” approach can resolve issues associated with carbon ecosystems. The conceptualized relationships deduced among multiple domains, integration of domain ontologies, data mining, visualization, and interpretation artefacts are highlights of the study. Several data, plot, and map views are extracted from metadata storage for interpreting new knowledge on carbon emissions. Statistical mining models describe data attributes’ correlations, patterns, and trends that can help in predicting future forecast of CO₂ emissions worldwide.     

Modeling seasonal variations of long-term soil CO<Subscript>2</Subscript> emissions in an orchard plantation in a semiarid area,SE Turkey

Emissions of soil CO₂ under different management systems have a significant effect on the carbon balance in the atmosphere. Soil CO₂ emissions were measured from an apricot orchard at two different locations: under the crown of trees (CO₂-UC) and between tree rows (CO₂-BR). For comparison, one other measurement was performed on bare soil (CO₂-BS) located next to the orchard field. Analytical data were obtained weekly during 8 years from April 2008 to December 2016. Various environmental parameters such as air temperature, soil temperature at different depths, soil moisture, rainfall, and relative humidity were used for modeling and estimating the long-term seasonal variations in soil CO₂ emissions using two different methods: generalized linear model (GLM) and artificial neural network (ANN). Before modeling, data were randomly split into two parts, one for calibration and the second for validation, with a varying number of samples in each part. Performances of the models were compared and evaluated using means absolute of estimations (MAE), square root of mean of prediction (RMSEP), and coefficient of determination (R²) values. CO₂-UC, CO₂-BR, and CO₂-BS values ranged from 11 to 3985, from 9 to 2365, and from 8 to 1722 kg ha^?1 week^?1, respectively. Soil CO₂ emissions were significantly correlated (p?<?0.05) with some environmental variables. The results showed that GLM and ANN models provided similar accuracies in modeling and estimating soil CO₂ emissions, as the number of samples in the validation data set increased. The ANN was more advantageous than GLM models by providing a better fit between actual observations and predictions and lower RMSEP and MAE values. The results suggested that the success of environmental variables for estimations of CO₂ emissions using the two methods was moderate.