Climate Change Mitigation Plans Research Articles

Modelling long-term Greenhouse Gas mitigation pathways for Mongolia

Abstract Mongolia’s faces substantial challenges to reduce its Greenhouse Gas (GHG) emissions due to a reliance on coal to meet electricity, heat and other energy demand and large livestock emissions. Despite this, Mongolia has committed to reduce GHG emissions by 22.7% in 2030 compared to a baseline emission scenario. Greenhouse gas mitigation assessments for Mongolia have focussed on actions to achieve emission reductions by 2030, but not on the potential for further reduction over the longer-term. This study addresses this gap through the development of long-term (2050) GHG emission pathways for Mongolia. From a technical mitigation potential perspective, these pathways aim to inform what level of GHG reduction targets Mongolia could commit to for the period after its current target expires. Greenhouse gas emissions are quantified for historic years (2010-2019) for all major GHG emitting source sectors, and projected to 2050 based on the population and socioeconomic development outlined in Mongolia’s Sustainable Development Vision 2050. Finally, mitigation scenarios that model implementation of 49 specific mitigation measures are developed, to quantify their emission reduction potential. Without mitigation, GHG emissions were estimated to increase by 67% and 139% in 2030 and 2050, respectively, compared to 2019 levels (39 million tonnes CO2-eq emissions). Implementation of the 49 mitigation measures could reduce GHG emissions by 40% in 2050 compared to the baseline. This study highlights that post-2030, Mongolia could increase its climate change mitigation ambition. Renewable Electricity generation, and energy efficiency were the measures that could achieve the majority of these additional emission reductions. However, despite the achieve emission reductions in excess of those within current commitments, there remains a substantial gap between the emission reduction potential of technically feasible measures currently being considered in Mongolia’s climate change mitigation planning, and full decarbonisation.

Connecting Global Water Agendas: From Wicked Problematization to Opportunities for Action

AbstractGlobal water security is one of humanities greatest challenges, yet it also represents an opportunity to catalyze transformative change. The very properties of water security that make it difficult to address also introduce potential for it to connect global agendas like climate change mitigation and adaptation planning and the sustainable development goals. We show that intergovernmental and scientific global agendas on water already embody this sentiment and highlight opportunities to better connect these agendas to accelerate action on water.

Harmonizing the complexities of financial development and fiscal decentralization in highly populated nations for monitoring environmental quality

The increasing challenges posed by global warming underscore the need to examine the contributing factors comprehensively. This study explores the complex interaction that enhances the financial autonomy of local governments (fiscal decentralization), the evolution of financial development, and the dynamics of the environment in China, India, and Pakistan, which are known as highly populated economies. This search fills a vacuum in the research literature by using financial development and fiscal decentralization together to measure the complex interaction by using the broad dataset that spans the years 1979–2019. It significantly advances scientific knowledge by highlighting the frequently disregarded triangle formed by fiscal decentralization, financial development, and environmental quality in emerging nations. Moreover, the study seeks to elucidate how fiscal decentralization affects financial growth and, in turn, the environment. The review acknowledges the possibility of varying results among nations. Strict econometric techniques that consider slope heterogeneity and cross-sectional dependence are used to guarantee the reliability of studies and conclusions. The results show that government money and GDP per capita have beneficial short-term effects on environmental quality. Simultaneously, financial growth positively and negatively impacts several factors, underscoring the complex dynamics at work in these crucial areas. Anticipated outcomes might provide a significant understanding of the fundamental factors influencing environmental quality in developing nations. This research is well-positioned to support the development of evidence-based policies by advising scholars and decision-makers on creating successful climate change mitigation plans.

A review on social work and climate change: the need for a transdisciplinary approach

ABSTRACT Climate change is a global emergency due to its social and economic impacts. This comprehensive review examines how climate change affects society and emphasises the role of social work in solving this problem with a multidisciplinary approach. Despite the least responsible, marginalised groups suffer hugely from climate change impacts and the global GDP will plummet, if no action is taken to mitigate its effects causing severe economic hardship for developing nations, and marginalised communities, which already face inequalities, resource shortages, and poor governance. There is a necessity for strategies to lower vulnerable population risks, thus emphasising the role of social work in addressing the inequalities of the climate change impacts. This paper discusses how social work concepts relate to environmental challenges and how they advocate for sustainability during climate change adaptation and mitigation, and promote environmental justice. Furthermore, the review also emphasises the need for transdisciplinary action on climate change. Scopus and Google Scholar databases was used to retrieve literature, based on the review questions, 51 literature items were selected for the evaluation and case examples. The review concludes that transdisciplinary action, including social workers’ role in decision-making and implementation, can create an equitable climate change mitigation plan.

The 2019–21 drought in southern Madagascar

Two consecutive failed rainy seasons in the southern part of Madagascar in 2019–21 had devastating impacts on the population, including an amplification of the ongoing food insecurity in the area. The drought events were second in severity only to the 1990–92 drought and were estimated in a previous study to have a return period of 135 years. In this study, the physical mechanisms that led to these consecutive drought events are investigated.We found that the anomalously cold sea surface temperatures (SSTs) that persisted to the south of Madagascar between December 2019 and December 2020 led to a decrease in the transport of moist air over land. These cold SST anomalies were the most negative anomalies in the past four decades and intensified the rainfall deficit resulting from a negative Subtropical Indian Ocean Dipole (SIOD) mode during the rainy season of December 2019 to March 2020 and during December 2020. We also found that the rainfall response to the SST anomaly south of Madagascar was three times greater than that of a canonical SIOD.A weak Mozambique Channel Trough and a strong Angola low system, on the other hand, modulated the expected above-normal rainfall from a La Niña event in January–February 2021. Our study demonstrates how local factors can modulate the impacts of large-scale drivers, and that both local and global drivers, and their interactions, should be considered when producing seasonal forecasts and advisories, as well as climate change adaptation and mitigation plans for southern Madagascar.

Reversible CO2 Capture and On-Demand Release by an Acidity-Matched Organic Photoswitch.

Separation of carbon dioxide (CO2) from point sources or directly from the atmosphere can contribute crucially to climate change mitigation plans in the coming decades. A fundamental practical limitation for the current strategies is the considerable energy cost required to regenerate the sorbent and release the captured CO2 for storage or utilization. A directly photochemically driven system that demonstrates efficient passive capture and on-demand CO2 release triggered by sunlight as the sole external stimulus would provide an attractive alternative. However, little is known about the thermodynamic requirements for such a process or mechanisms for modulating the stability of CO2-derived dissolved species by using photoinduced metastable states. Here, we show that an organic photoswitchable molecule of precisely tuned effective acidity can repeatedly capture and release a near-stoichiometric quantity of CO2 according to dark-light cycles. The CO2-derived species rests as a solvent-separated ion pair, and key aspects of its excited-state dynamics that regulate the photorelease efficiency are characterized by transient absorption spectroscopy. The thermodynamic and kinetic concepts established herein will serve as guiding principles for the development of viable solar-powered negative emission technologies.

Towards mega-scale decarbonized industrial park (Mega-DIP): Generative AI-driven techno-economic and environmental assessment of renewable and sustainable energy utilization in petrochemical industry

In alignment with climate change mitigation plans, renewable and sustainable energy integration into the industrial sector should be investigated for decarbonization purposes by 2050. This study aims to examine the feasibility of decarbonization of mega-scale industrial parks with two emerging technologies; first, an integrated energy system involving an air separation unit (ASU) and a liquid air energy storage (LAES)-based power generation system (ALPG) with the assistance of a large-scale liquified natural gas (LNG) plant and second, the green electricity predicted by various renewable energy (VRE) scenarios using an AI-driven generative model concerning techno-economic and environmental analyses. Firstly, the electricity demand of mega-scale petrochemical industrial complexes was allocated, and remote sensing data of VRE (wind and solar) were collected in South Korea. Then, the ALPG was modeled to consider the actual operation of an LNG plant near target complexes. Subsequently, the CC-informed VRE scenario generation model (CC-VRES) based on an adversarial autoencoder (AAE) algorithm was developed to generate VRE scenarios considering the CC effects. Finally, techno-economic and environmental assessments were conducted to target the mega-DIP by 2050. The results showed that the levelized cost of electricity of the proposed ALPG was 150 $/MWh, which is enough to supply the required sustainable electricity, considering the Korean energy plan and CC effects in the CC-VRES model. Therefore, the excess VRE can be used to decarbonize the target industrial parks; it is anticipated that 26,084 GW h/yr of renewable and sustainable electricity will be generated and cover the total electricity demand of Yeosu and Ulsan target complexes while reducing 13,943-kilo t CO2 eq./yr through 2050.

Flexibility assessment of power-hydrogen-power (P2H2P) system in multi-energy districts

Climate change mitigation plans and recent tension over gas supply generate significant incentives for reducing reliance on fossil fuels in the built-environment, with an increase in installing renewables and in electrifying the heating sector. However, the seasonal imbalance of energy supply and demand is expected to increase further. Sector coupling enabled through power-to-hydrogen-to-power (P2H2P) systems that include seasonal storage can partially mitigate this issue. In this work, a hydrogen-based multi-energy system is modelled with a mixed-integer linear programming based Ehub optimization tool. While results show that the P2H2P system is technically and spatially feasible and more flexible, its large hydrogen storage is associated with high costs and embodied carbon emissions. Finally, an outlook for P2H2P systems for integration into renewable-based systems is presented.

Small scale agriculture continues to drive deforestation and degradation in fragmented forests in the Congo Basin (2015–2020)

The Central African region hosts the largest continuous tract of forest in Africa, regulating global climate while providing essential resources and livelihoods for millions of people and harbouring extensive biodiversity. Extractive industries, infrastructure development and industrial agriculture have often been cited as major threats to these forests and are expected to increase. A regional collaborative effort has produced the first systematically validated remote sensing assessment of deforestation and degradation drivers in six central African countries for the 2015–2020 time period. Multiple, overlapping drivers are assessed through visual interpretation and show that the rural complex, a combination of small-scale agriculture, villages, and roads contributes to the majority of observed deforestation and degradation. Industrial drivers such as mining and forestry are less common, although their impacts on carbon and biodiversity could be more permanent and significant than informal activities. Artisanal forestry is the only driver that is observed to be consistently increasing over the study period. Our assessment produces information relevant for climate change mitigation and land use planning which requires detailed information on multiple direct drivers to target specific activities and investments.

Association between ambient temperature and common allergenic pollen and fungal spores: A 52-year analysis in central England, United Kingdom

Exposure to pollen and fungal spores can trigger asthma/allergic symptoms and affect health. Rising temperatures from climate change have been associated with earlier seasons and increasing intensity for some pollen, with weaker evidence for fungal spores. It is unclear whether climate change has resulted in changes in the exposure-response function between temperature and pollen/fungal spore concentrations over time. This study examined associations between temperature and pollen/fungal spores in different time periods and assessed potential adaptation using the longest pollen/fungal spore dataset in existence (52 years). Daily concentrations of pollen (birch and grass) and fungal spores (Cladosporium, Alternaria, Sporobolomyces and Tilletiopsis) collected between April and October from Derby (1970–2005) and Leicester (2006–2021), UK, were analysed. Cumulative seasonal concentrations (seasonal integral) and start-of-season were calculated and linked to seasonal mean temperatures (Tmeans) using generalized additive models. Daily concentrations were evaluated against daily Tmean with distributed lagged nonlinear models. Models were adjusted for precipitation, relative humidity, long-term trend and location. Seasonal and daily analyses were respectively stratified into two periods (1970–1995, 1997–2021) and five decades. Warmer seasonal Tmeans were associated with higher seasonal integral for birch, Cladosporium and Alternaria, as well as earlier start-of-season for birch, grass and Cladosporium. There were indications of changing associations with temperature in the recent decades. A warmer January was associated with higher seasonal integral for grass in 1997–2021, but not in 1970–1995. In 2000–2021, daily concentrations of birch pollen tended to remain at higher levels, vs. decrease during 1990s, when Tmean was between 13 and 15 °C. Our study suggests higher temperatures experienced in recent decades are associated with higher overall abundance of some pollen/fungal spores, which may increase future disease burdens of allergies. The changing responses of some pollen to higher temperatures over time may indicate adaptation to increasing temperatures and should be considered in climate change mitigation and adaptation planning.

Temporal and Spatial Analysis of Rainfall and Evapotranspiration in Erbil Plain and the Peripheral Areas

Since analysis of spatial events is region specific and cannot be generalized, local studies are of vital importance to identify the most accurate interpolation methods. Furthermore, the trend analysis of climatic parameters at different time scales is helpful for making a better climate change adaptation and mitigation plan to overcome water scarcity. Accordingly, the current study was proposed to detect trends in rainfall and evapotranspiration at monthly and annual time scales over the Erbil plain and the surrounding area using parametric and non-parametric tests. Moreover, four deterministic and five geostatistical methods were evaluated for searching the best interpolation method to generate a continuous surface for the indicated climatic variables. The results revealed that the majority of data sets are categorized useful class and serially independent. Furthermore, it was found both rainfall and potential evapotranspiration have a mix of upward and downward trends and most of them are insignificant at 5% level of significance. Further, the interpolation analysis indicated that local polynomial interpolation (LPI) method was proven to be best interpolator for generating continuous surfaces for rainfall and ETo over the area under study followed by the empirical Bayesian Kriging method.

A framework for assessing the sustainable transition potential of municipal climate change mitigation plans

Local climate change mitigation plans can be essential in enabling sustainability transitions at the municipality level. However, existing frameworks for assessing the quality of climate plans inadequately address their potential to foster sustainability transitions. We develop an assessment framework consisting of indicators and associated questions by integrating elements of the transition management framework with essential quality dimensions of climate plans and planning processes identified through a literature review. We illustrate and validate the assessment framework by applying it to the climate plans of three medium-sized cities in Switzerland. Our findings demonstrate that the local climate change mitigation plans performed well regarding the tactical and operational indicators but were less optimal concerning strategic and reflexive indicators. Nevertheless, the transition management framework provided a useful framework contributing to a comprehensive and systemic assessment highlighting the importance of stakeholder involvement, inter-sectoral coordination, and monitoring and evaluation as a means for social learning.

Assessment of NASA POWER for Climate Change Analysis using the De Martonne Climate Index in Northern Peninsular Malaysia

Global warming is threatening human population, one of which is the frequent climate disasters, i.e., floods and drought, which affect the global food security. However, the assessment of regional climatic changes is constrained by the lack of high-quality long-term climate data. Taking the Northern Region of Peninsular Malaysia as the study area, this paper aims to assess the performance of NASA POWER in estimating annual and seasonal variability of the climate variables via De Martonne climate indices from 1985 to 2021. The findings shows that NASA POWER performed satisfactory in estimating both mean temperature and rainfall over the northern Peninsular Malaysia. The NASA POWER project performs the best in Bayan Lepas station with C = 0.70 for annual mean temperature while for annual rainfall is at the Ipoh station with C = 0.50. The annual De Martonne climate index shows that the study area is dominated by wet climate conditions, since the index classify the climate as “very humid” to “extremely humid”. The De Martonne climate indes is calculated from both data source is very similar. Therefore, NASA POWER data is useful in climate studies, while the knowledge of climate variability is beneficial for climate change mitigation and adaptation plans, particularly for agriculture.

A high-resolution monitoring approach of urban CO2 fluxes. Part 2 – surface flux optimisation using eddy covariance observations

Achieving climate neutrality by 2050 requires ground-breaking technological and methodological advancements in climate change mitigation planning and actions from local to regional scales. Monitoring the cities' CO2 emissions with sufficient detail and accuracy is crucial for guiding sustainable urban transformation. Current methodologies for CO2 emission inventories rely on bottom-up (BU) approaches which do not usually offer information on the spatial or temporal variability of the emissions and present substantial uncertainties. This study develops a novel approach which assimilates direct CO2 flux observations from urban eddy covariance (EC) towers with very high spatiotemporal resolution information from an advanced urban BU surface flux model (Part 1 of this study, Stagakis et al., 2023) within a Bayesian inversion framework. The methodology is applied to the city centre of Basel, Switzerland (3 × 3 km domain), taking advantage of two long-term urban EC sites located 1.6 km apart. The data assimilation provides optimised gridded CO2 flux information individually for each urban surface flux component (i.e. building heating emissions, commercial/industrial emissions, traffic emissions, human respiration emissions, biogenic net exchange) at 20 m resolution and weekly time-step. The results demonstrate that urban EC observations can be consistently used to improve high-resolution BU surface CO2 flux model estimations, providing realistic seasonal variabilities of each flux component. Traffic emissions are determined with the greatest confidence among the five flux components during the inversions. The optimised annual anthropogenic emissions are 14.7 % lower than the prior estimate, the human respiration emissions have decreased by 12.1 %, while the biogenic components transformed from a weak sink to a weak source. The root-mean-square errors (RMSEs) of the weekly comparisons between EC observations and model outputs are consistently reduced. However, a slight underestimation of the total flux, especially in locations with complex CO2 source/sink mixture, is still evident in the optimised fluxes.

Accelerated vegetative growth measured by gross primary productivity in China from 1980 to 2018

Vegetation response to climate change can be characterized as long-term trends, but with periodic oscillations. Revealing vegetation response trends, with oscillations, is essential for formulating climate change mitigation and adaptation planning, as well as adopting ecological protection and restoration measures. Here, the long-term trend and periodic oscillations in vegetation changes were explored through a measure of gross primary production (GPP), an indicator of vegetation photosynthesis, using data of both high spatial resolution (1 km2) and a long-term of nearly 4 decades, from 1980 to 2018. The GPP data were estimated for China’s terrestrial ecosystem and validated against the eddy covariance-based observations of 70 site-year in China and compared to other GPP data products. The results showed the estimated GPP can explain 76% of spatial–temporal variance with the same data accuracy as other products, but with the advantage of both high spatial resolution and long time periods that other products do not have. From the estimated GPP data, Chinese terrestrial vegetation growth was found to have a periodic oscillation of close to 2.79 years and a long-term increasing trend of 59.8 Mt C per year over 92% of all vegetated land in China from 1980 to 2018. Moreover, it was found that vegetation growth accelerated significantly in the two recent decades over the previous two decades. The growth rate was 83.5 Mt C a−1 from 2000 to 2018, which was 2.31 times faster than the rate from 1980 to 2000. The underlying mechanism of the vegetation responses was analyzed by ridge regression, a method to decrease the effects of multicollinearity. Long-term variability and trends in vegetation could be attributed to a warming minimum air temperature when examined with precipitation, maximum air temperature, and solar shortwave radiation. Though more influences, such as those from freezing and thawing and human activities, should be explored in the future, this study offers insight that warming, with increasing daily minimum temperatures, is likely to be a vital contributor to ecosystem changes, and will be essential knowledge for mitigation of effects and adaptation to future climate.

Coconut-based agroecosystem for carbon sequestration

Coconut (Cocos nucifera L.) is a perennial crop that provides a staple food and serves as a cash crop in many developing countries. Its production is mostly affected by climate, soil and diseases. The threat posed by greenhouse gases (GHGs) emissions especially carbon dioxide (CO2) responsible for global warming and climate change, has called for the urgent need to mitigate climate change, by exploring environmental friendly ways to sequester CO2 from the atmosphere. The coconut farming and its agroecosystem is one of the ways that could substantially store CO2 through sequestration and will help to reduce the current increase in CO2 present in the atmosphere. Although, coconut plantations have similar characteristics and functions with tropical forests, it has ability to sequester carbon better than tropical forests. Besides coconut farming is improving income and livelihood of farmers, it’s therefore, paramount to utilize the potential of coconut-based agroecosystem for carbon sequestration, and investment opportunity needed for carbon trading, and as well help in climate change adaptation and mitigation plan.

Business Models for Energy Community in the Aggregator Perspective: State of the Art and Research Gaps

Distributed Energy Resources (DERs) are spreading under the pressure of climate change mitigation plans and the framework, recognized as the most suitable to exploit DER diffusion, is the Energy Community (EC). Understanding the role of energy companies, especially Aggregators, in this context, is still an open topic, as it is not clear how they can support members in the aggregation process and how they create value through their business. The aim of the study is therefore to revise whatever is currently present in the research agenda and consequently a systematic literature review has been carried out. The contribution of this work consists of illustrating the main features of Aggregators, pointing out how they implement their strategies in the energy markets, with which services they capture value, who their partners and customers are, what the financial aspects are of their activities with respect to the size of the aggregated clusters, and, in conclusion, which are the main business model structures currently deployed. Then, considerations are made concerning EC context, identifying the areas where an Aggregator could usefully support communities’ establishment and management, solving well-known hindrances, and what gaps future research should fill.

Barrier analysis to leverage the climate change mitigation-adaptation implementation action in mangrove forest and its surrounding community villages

In the Nationally Determined Contribution document (NDC), Indonesia has a target for reducing greenhouse gas emissions of 29% on its own capability and 41% with international assistance by 2030, and plans to initiate action to achieve net zero emissions by 2050 as outlined in the Long-term Strategy for Low Carbon and Climate Resilience (LTS-LCCR) 2050 document. Mangrove forest ecosystems can absorb carbon emissions 4-5 times greater than terrestrial forests, are used as one of source to achieve Indonesia’s NDC and LTS-LCCR targets. Mangrove forest is also as a barrier to coastal abrasion. Mangrove forests can be damaged by natural cause and also due to human activities. Socialization of its importance and improving the economic community welfare around mangrove forests are important factors to prevent damage and maintain its sustainability. The improving economic community efforts can be carried out by applying mitigation-adaptation actions/economic activities of the community. This activity aims to analyse the barrier and challenges in preserving and restoring, as well as rehabilitating the mangrove forests ecosystem in the Indramayu and Brebes regencies. The data obtained from field surveys while the methodology for barrier analysis refers to the methodology that has been used in the preparation of Indonesia’s Climate Change mitigation and adaptation Technology Action Plans (TAPs) documents. The barrier analysis includes regulatory/policy, technical, financial, coordination, and social and/or institutional barrier aspects. These barriers have been analysed, mapped and identified, then causal relations of the barriers have been carried out and identified. Then the cross-cutting issued/integrated the measures of both issues and prioritized the cross-cutting issued can be determined.

Pantropical distribution of short-rotation woody plantations: spatial probabilities under current and future climate

Short-rotation woody plantations (SRWPs) play a major role in climate change mitigation and adaptation plans, because of their high yields of woody biomass and fast carbon storage. However, their benefits, trade-offs and growing-success are heavily location-dependent. Therefore, spatial data on the distribution of SRWPs are indispensable for assessing current distribution, trade-offs with other uses and potential contributions to climate mitigation. As current global datasets lack reliable information on SRWPs and full global mapping is difficult, we provide a consistent and systematic approach to estimate the spatial distribution of SRWPs in (sub-)tropical biomes under current and future climate. We combined three advanced methods (maximum entropy, random forest and multinomial regression) to evaluate spatially explicit probabilities of SRWPs. As inputs served a large empirical dataset on SRWP observations and 17 predictor variables, covering biophysical and socio-economic conditions. SRWP probabilities varied strongly between regions, and might not be feasible in major parts of (sub-)tropical biomes, challenging the feasibility of global mitigation plans that over-rely on tree plantations. Due to future climatic changes, SRWP probabilities decreased in many areas, particularly pronounced in higher emission scenarios. This indicates a negative feedback with higher emissions resulting in less mitigation potential. Less suitable land for SRWPs in the future could also result in fewer wood resources from these plantations, enhancing pressure on natural forests and hampering sustainability initiatives that use wood-based alternatives. Our results can help adding a more nuanced treatment of mitigation options and forest management in research on biodiversity and land use change.

From least cost to least risk: Producing climate change mitigation plans that are resilient to multiple risks

Our plans to tackle climate change could be thrown off-track by shocks such as the coronavirus pandemic, the energy supply crisis driven by the Russian invasion of Ukraine, financial crises and other such disruptions. We should therefore identify plans which are as resilient as possible to future risks, by systematically understanding the range of risks to which mitigation plans are vulnerable and how best to reduce such vulnerabilities. Here, we use electricity system decarbonization as a focus area, to highlight the different types of technological solutions, the different risks that may be associated with them, and the approaches, situated in a decision-making under deep uncertainty (DMDU) paradigm, that would allow the identification and enhanced resilience of mitigation pathways.