Transitioning to a green economy is urgently needed to achieve the climate targets by the end of this century. Here we investigate alternative pathways for the transition of the global economy from one dominated by the fossil-fueled (brown) sector to one dominated by the low-emission (green) sector. We modify a well-known integrated assessment model of climate change and economy to consider three transition pathways: Linear, Delayed, and Fast. Our results indicate that the main burden of the green transition lies on capital formation, accumulation, and transfers facilitated by full R&D investment in the green sector’s productivity. We also find that transition pathways rely on different mechanisms to achieve their targets. The Delayed pathway relies on the combination of higher green capital investment and an increase in green capital productivity through R&D investment, while the Fast pathway requires substantial transfers of capital from the brown sector coupled with high abatement efforts.
Agriculture in India has undergone significant changes since independence. Though technological advancements in agriculture have brought enormous growth in variation and production in India, it has also caused soil degradation in different regions because of indiscriminate deforestation, heavy irrigation, excessive use of fertilizers and pesticides, introduction of alien varieties and burning of the crop residues. Various studies have shown that degradation of soil will affect the qualitative and quantitative outcomes in agriculture as well as soil biodiversity, which could be controlled through sustainable agriculture and soil management. The Land Degradation Neutrality (LDN) program initiated by the United Nations Convention to Combat Desertification (UNCCD) represents a paradigm shift in land management policies and practices. On a municipal level, this requires effective legal regulations, policies interventions, market incentives, digital documentation and survey of the private and common land. However, there is no ‘soil law’ per se in India, though conservation and management of soil is facilitated through national and provincial agricultural, environmental, land and waste management laws and policies. The chapter aims specifically to examine agriculture legislations, regulations, policies, missions and schemes addressing the issues related to soil degradation in India. It proposes for the effective and coordinated action to restrain and reverse the soil degradation in view of its soil related international obligations and national commitments.
This article examines the Sulcis coal region in Italy and illustrates how discursive dynamics can impede energy transition by delegitimizing coal decline and the diffusion of renewable energies. Combining quantitative analyses of textual data and argumentative discourse analysis, we analyze newspaper articles published between 2011 and 2021 in the national, regional, and local press. Our findings reveal that shifts in topic salience and storylines reflect different transition phases (coal legitimacy, regime destabilization, and reconfiguration). Throughout the analyzed period, newspapers have cultivated a discursive environment that weakens efforts to phase out coal and promote low-carbon energy by amplifying particular storylines endorsed by competing discourse coalitions. Media discourse consistently portrays decarbonization and coal phase-out as threatening, anticipating disruption to regional livelihoods and traditions. Over time, renewable energies are marginalized or hindered by storylines promoting regime stability (coal legitimacy), soft transformation (coal-to-gas transition), and, finally, a reconfiguration (utility-scale renewable transition) promoted by incumbents and resisted by locally based discourse coalitions perceiving it as a form of colonialism. This study sheds light on the interplay between discourse dynamics and the complexities and challenges of the destabilization–reconfiguration pathway of coal regions. It contends that approaches combining both build-up and break-down dynamics into the analysis of transitions can offer a more nuanced, politically sensitive understanding and practical insights to instigate and navigate more equitable destabilization–reconfiguration pathways.
Buildings are key in supporting human activities and well-being by providing shelter and other important services to their users. Buildings are, however, also responsible for major energy use and greenhouse gas (GHG) emissions during their life cycle. Improving the quality of services provided by buildings while reaching low energy demand (LED) levels is crucial for climate and sustainability targets. Building sector models have become essential tools for decision support on strategies to reduce energy demand and GHG emissions. Yet current models have significant limitations in their ability to assess the transformations required for LED. We review building sector models ranging from the subnational to the global scale to identify best practices and critical gaps in representing transformations toward LED futures. We focus on three key dimensions of intervention (socio-behavioral, infrastructural, and technological), three megatrends (digitalization, sharing economy, and circular economy), and decent living standards. This review recommends the model developments needed to better assess LED transformations in buildings and support decision-making toward sustainability targets.
Tropical Cyclones (TCs) are counted among the most destructive phenomena that can be found in nature. Every year, globally an average of 90 TCs occur over tropical waters, and global warming is making them stronger and more destructive. The accurate localization and tracking of such phenomena have become a relevant and interesting area of research in weather and climate science. Traditionally, TCs have been identified in large climate data sets through the use of deterministic tracking schemes that rely on subjective thresholds. This study presents a Machine Learning (ML) ensemble approach for locating TCs center coordinates. The ensemble combines TCs center estimates of different ML models that agree about the presence of a TC in input data. ERA5 reanalysis data was used for model training and testing jointly with the International Best Track Archive for Climate Stewardship (IBTrACS) records. Compared to single models estimates, the ML ensemble approach was able to improve TCs localization in terms of Euclidean Distance with respect to the observed TCs locations from IBTrACS. Moreover, a hybrid tracking scheme was defined: starting from the individual TC center locations detected by the ML ensemble approach, a deterministic tracking algorithm was used for reconstructing TC trajectories. The hybrid tracking scheme was then compared with four deterministic trackers reported in literature, achieving a Probability of Detection and a False Alarm Rate of 71.49% and 23%, respectively, over 40 years of reanalysis data.
Energy models are used to study emissions mitigation pathways, such as those compatible with the Paris Agreement goals. These models vary in structure, objectives, parameterization and level of detail, yielding differences in the computed energy and climate policy scenarios. To study model differences, diagnostic indicators are common practice in many academic fields, for example, in the physical climate sciences. However, they have not yet been applied systematically in mitigation literature, beyond addressing individual model dimensions. Here we address this gap by quantifying energy model typology along five dimensions: responsiveness, mitigation strategies, energy supply, energy demand and mitigation costs and effort, each expressed through several diagnostic indicators. The framework is applied to a diagnostic experiment with eight energy models in which we explore ten scenarios focusing on Europe. Comparing indicators to the ensemble yields comprehensive ‘energy model fingerprints’, which describe systematic model behaviour and contextualize model differences for future multi-model comparison studies.
The Land-Use, Land-Use Change and Forestry sector (LULUCF) role is of critical importance in contributing to the ambitious targets set by the European Union (EU) to reduce by 55% net greenhouse gas (GHG) emissions by 2030, compared to 1990 levels, and to become carbon neutral by 2050. The EU LULUCF regulation, approved in 2023, sets out binding targets for each individual Member State to be achieved by 2030, totaling 310 MtCO2e of net removals for the whole EU. However, it remains poorly understood to what extent the EU LULUCF climate target matches with the Member States’ strategies. The alignment between the EU governance and its Member States’ visions for the long-term will determine the achievement of the climate targets. The objective of this study is to understand the LULUCF expected contribution to the EU’s 2030 and 2050 climate goals. In doing so, we explored the European and country-level visions of LULUCF with respect to climate change mitigation and adaptation, as expressed in their Long-Term Strategies (LTSs) and national projections; we evaluated whether national level projections for LULUCF are aligned with the EU short and long term targets. We found that most countries’ LTSs envisage policies and measures in the LULUCF sector, however they are, often general and not comprehensive. Furthermore, the majority of countries’ quantitative future projections of GHG emissions and removals from LULUCF differ from the pathway set in EU targets; thus, countries may need to either update existing policies or conceive and plan new policies and actions.
The interplay of a warming climate and socio-demographic transformations will increase global heat exposure. Assessing future use and impacts of energy-intensive appliances for indoor thermal adaptation is therefore a crucial policy goal. Here we train statistical models on multi-country household survey data (n = 480,555) to generate global gridded projections of residential air-conditioning (AC) uptake and use. Our results indicate that the share of households owning AC could grow from 26% to a scenario median of 38% by 2050, implying a doubling of residential AC electricity consumption, to 925 TWh/yr. This growth will be highly unequal both within and across countries and income groups, with significant regressive impacts. Up to 4.5 billion heat-exposed people may lack AC access in 2050. Outcomes will largely depend on socio-economic development and climate change pathways. Our gridded projections can support the modelling of the impacts of residential AC on decarbonization pathways and health outcomes.
Geoengineering, including solar radiation management (SRM), has received increasing scrutiny due to the rise of climate extremes and slow progress in mitigating global carbon emissions. This climate policy option, even as a possibility, can have consequential implications for international climate governance. Here, we study how solar engineering affects the effectiveness and stability of a large set of regional coalitions through numerical simulations. We posit a requirement in terms of global political or economic power and analyze the exclusive membership coalition formation process when coalitions jointly decide on geoengineering and mitigation. We show that geoengineering can provide incentives for cooperation and partially solve the typical trade-off between stability and effectiveness of climate coalitions. However, temperature reduction mostly comes from deploying SRM within the coalition rather than from further emission reductions, thus exposing the world to relatively large-scale deployment of SRM with as of today uncertain potential side effects and risks.
The advancement of computational resources has allowed researchers to conduct convection-permitting regional climate model (CPRCM) simulations. A pioneering effort promoting a multimodel ensemble of such simulations is the CORDEX Flagship Pilot Studies (FPS) on "Convective Phenomena over Europe and the Mediterranean" over an extended Alps region. In this study, the Distribution Added Value metric is used to determine the improvement of the representation of all available FPS hindcast simulations for the daily mean wind speed. The analysis is performed on normalized empirical probability distributions and considers station observation data as the reference. The use of a normalized metric allows for spatial comparison among the different regions (coast and inland), altitudes and seasons. This approach permits a direct assessment of the added value between the CPRCM simulations against their global driving reanalysis (ERA-Interim) and respective coarser resolution regional model counterparts. In general, the results show that CPRCMs add value to their global driving reanalysis or forcing regional model, due to better-resolved topography or through better representation of ocean-land contrasts. However, the nature and magnitude of the improvement in the wind speed representation vary depending on the model, the season, the altitude, or the region. Among seasons, the improvement is usually larger in summer than winter. CPRCMs generally display gains at low and medium range altitudes. In addition, despite some shortcomings in comparison to ERA-Interim, which can be attributed to the assimilation of wind observations on the coast, the CPRCMs outperform the coarser regional climate models, both along the coast and inland.
The present study attempts to unravel the various factors that influence the evolution of lightning events during premonsoon season over the Northwest Himalayas which experiences the highest frequency of lightning across the Indian subcontinent. Preliminary investigations depict a positive agreement between lightning and rainfall activity, which is also known to be strongly affected by global teleconnection processes, according to previous studies. Subsequent statistical analysis further ascertained the large-scale influence on lightning occurrences, which is also supported by the regional ice water content and upper tropospheric divergence patterns. Next, this hypothesis was tested on months with contrasting PDO values which also revealed a congruent variation in their corresponding lightning properties thereby warranting the proposed causality.
Coastal regions around the world are experiencing the increasing threat of sea level rise (SLR) due to climate change. Predictive models are needed to identify areas at risk of flooding and plan future activities aimed at avoiding, or at least limiting, damage to the natural environment, buildings, and people. Geographic Information Systems (GISs), through the processing of georeferenced data, allow not only to map the coastal areas that may be submerged, but also to carry out calculations and analysis to support further studies and insights. This paper examines the impact of SLR on the coasts of Sicily, Italy, using a Digital Elevation Model (DEM) of the study area (resolution: 20 m x 20 m) and GIS tools. The objective is to provide medium scale maps of the potential submerged zones for the end of the 21st century (2071-2100) as resulting from SLR values supplied by the Copernicus platform and identified as the IPCC (CMIP5) RCP4.5 scenario. The bathtub method available in literature is carried out using Quantum GIS (QGIS) software version 3.28. Attention is also focused on the limits and advantages of the adopted approach. The experiments demonstrate on the one hand the versatility of the GIS tools that allow the implementation of the bathtub method, on the other the seriousness of the SLR, given the considerable extension of the areas at risk of submersion as resulting from the maps produced for Sicily coasts.
Adapting to climate change involves taking a series of actions that reduce and/or avoid the effect of climate risks while ultimately increasing development opportunities in affected environments. Therefore, adaptation to climate change must become an integral part of a sustainable development process, in which it maintains the same priority as other development goals and strategies. Aiming to address the conceptual gap in coherent policy research in the fields of climate change and sustainable development, we performed coherence analysis research, categorized the different approaches, and defined methodology. The methodological framework was tested during the Living Lab based on a deliberative participatory process, making it easily applicable to diverse targets and contexts. The methodological framework developed in this study represents an unprecedented experience at the national (Italy) and regional level (Sardinia), projected to address global current/future environmental issues and problems through local knowledge, peculiarities, and resources.
Stochastic simulations of virtual oil spills from ships were performed for the Adriatic Sea over 2017-2020, applying the European Marine Observation and Data Network vessel densities as a proxy for starting locations of operational spillage. The MEDSLIK-II oil spill model was run using high-resolution currents provided by the Copernicus Marine Service and the European Centre for Medium-Range Weather Forecasts winds. Chronic exposure to operational oil spills was reported in terms of hazard indices for five vessel groups: pleasure and passenger ships, cargo and service vessels, the fishing fleet, tankers, and other ships. The northernmost Adriatic expectedly showed the highest hazard values, including the areas of Trieste and Venice, where cargo and service ships were the dominant polluters. The Croatian coastal waters were more chronically polluted than the Italian coastal waters; the predominant contribution was from coastwise pleasure and passenger ships.
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