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This paper addresses some questions that climate change raises for international law. It focuses in particular on the request for an advisory opinion submitted by the United Nations General Assembly to the International Court of Justice (ICJ) on 29 March 2023. This request is analysed as a further manifestation of the international community’s concern to clarify the international responsibility of states to prevent, mitigate and remedy the damage caused by climate change. The study argues that the ICJ could clarify obligations under existing treaties. There may also be a particular opportunity for the Court to expand its jurisprudence on other sources of international law. In particular, it asks whether the ICJ could confirm the emergence of a specific customary rule on the issue.

SummaryInequality has recently become a major concern in economics. Leaving aside its social and economic effects is also possible to trace its environmental consequences, which this article attempts to assess. The indicator to be measured is the household's carbon footprint (CF) for different social groups. The deep economic crisis in the Spanish economy between 2008 and 2013 has increased consumption inequality and doubled the number of households below the poverty line. When focusing on domestic consumption, we found that the shopping basket of all income groups has very similar emissions intensities; therefore, the differences among the household CFs depend mainly on the scale effect (i.e., the size of consumption). However, when international trade is also considered, we found that the emission intensity of imports is bigger than the intensity of all the respective domestic goods. Therefore, the share of imported goods and services by social class will be an important determinant of the respective total CF. Before the crisis, households with higher incomes imported 30% of their total consumption items whereas households with lower income imported only 20% of their consumption. During the crisis, the imports of medium‐high‐income households fell to 20%, whereas low‐ and middle‐income families maintained the same import share, which contributed to the reduction of the total household CF.

Nowadays, the issues related with environment preservation assume an increasing importance. Progressively, more sustainable solutions/techniques are being developed to combat environmental destruction. The decision to include themes related to the environment in the curriculum of technological courses in higher education aims to promote more sustainable behaviors and in an indirect way, increase the environmental literacy of the population. Thus, this study aims to evaluate the environmental literacy focusing on four topics, i.e., air pollution, water pollution, global warming, and energy resources. For this purpose, a questionnaire was developed and applied to a convenience sample, formed by individuals of both genders, aged between 20 and 81 years old. The questionnaire intended to collect data to characterize the sample and assess the literacy regarding environmental issues. In order to carry out the environmental literacy assessment, the respondents were asked to express their degree of agreement with some statements related with the environmental themes mentioned above. The data collected was analyzed using data mining tools. The results suggest that the population’s literacy is satisfactory in relation to some issues, but insufficient in relation to others, equally important, but less disseminated.

Nowadays in current cities the increasing levels of pollution emissions and fuel consumption derived from the road traffic directly affect to the air quality, the economy, and specially the health of citizens. Therefore, improving the traffic flow is a mandatory task in order to mitigate such critical problems. In this work, we propose a Swarm Intelligence approach for optimizing signal light timing programs in metropolitan areas. In this way, we can improve the traffic flow of vehicles with the global target of reducing their fuel consumption and gas emissions (CO and NOx). In this article we optimize the timing programs of signal lights and analyze their effect in pollution by following the standard HBEFA as traffic emission model. In concrete, we are focused here on two large and heterogeneous urban instances located in the cities of Malaga and Seville (in Spain). In comparison with timing programs of signal lights predefined by experts (close to real ones), our proposal obtains significant reductions in terms of the emission rate and the total fuel consumption. Junta de Andalucía P07-TIC-03044 Ministerio de Ciencia e Innovación TIN2011-28194 Ministerio de Ciencia e Innovación TIN2008-06491-C04-01 Ministerio de Ciencia, Innovación y Universidades BES-2009-018767

AbstractAimFuture climate change threatens marine forests across the world, potentially disrupting ecosystem function and services. Nonetheless, the direction and intensity of climate‐induced changes in kelp forest biodiversity remain unknown, precluding well‐informed conservation and management practices.LocationGlobal.MethodsWe use machine‐learning models to forecast global changes in species richness and community composition of 105 kelp forest species under contrasting Shared Socioeconomic Pathway (SSP) scenarios of climate change (decade 2090–2100): one aligned with the Paris Agreement and another of substantially higher emissions.ResultsA poleward and depth shift in species distributions is forecasted, translating into ~15% less area in the extent of the global biome, coupled with marked regional biodiversity changes. Community composition changes are mostly projected in the Arctic, the Northern Pacific and Atlantic, and Australasia, owing to poleward range expansions and wide low latitude losses.Main ConclusionsBy surpassing the Paris Agreement expectations, species reshuffling may simplify and impair ecosystem services in numerous temperate regions of Australasia, Southern Africa, Southern America and the Northern Atlantic, and in the tropical Pacific, where complete species losses were projected without replacement. These estimates, flagging threatened regions and species, as well as refugial areas of population persistence, can now inform conservation, management and restoration practices considering future climate change.

The global energy system subsumes both extreme wealth (and waste) and extreme poverty. A minority of the global population is consuming the majority of the fossil fuel-based energy and causing global warming. While the mature industrialized economies maintain their high levels of energy consumption, the emerging economies are rapidly expanding their fossil energy systems, emulating traditional patterns of industrialization. We take a global, socio-metabolic perspective on the energy transition phases—take-off, maturation, and completion—of 142 countries between 1971 and 2015. Even within our global fossil energy system, the transition to fossil energy is still ongoing; many countries are in the process of replacing renewable energy with fossil energy. However, due to globally limited supplies and sinks, continuing the fossil energy transition is not an indefinite option. Rather than a “Big Push” for renewable energy within pockets of the fossil energy system, a sustainability transformation is required that would change far more than patterns of energy supply and use. Where this far-reaching change requires pushing back against the fossil energy system, the energy underdogs—the latecomers to the fossil energy transition—just might come out on top.

A novel multi-criteria methodology for the identification of defective anemometers is shown in this paper with a benchmarking approach: it is called MIDAS: multi-technique identification of defective anemometers. The identification of wrong wind data as provided by malfunctioning devices is very important, because the actual power curve of a wind turbine is conditioned by the quality of its anemometer measurements. Here, we present a novel method applied for the first time to anemometers’ data based on the kernel probability density function and the recent reanalysis ERA5. This estimation improves classical unidimensional methods such as the Kolmogorov–Smirnov test, and the use of the global ERA5’s wind data as the first benchmarking reference establishes a general method that can be used anywhere. Therefore, adopting ERA5 as the reference, this method is applied bi-dimensionally for the zonal and meridional components of wind, thus checking both components at the same time. This technique allows the identification of defective anemometers, as well as clear identification of the group of anemometers that works properly. After that, other verification techniques were used versus the faultless anemometers (Taylor diagrams, running correlation and R M S E , and principal component analysis), and coherent results were obtained for all statistical techniques with respect to the multidimensional method. The developed methodology combines the use of this set of techniques and was able to identify the defective anemometers in a wind farm with 10 anemometers located in Northern Europe in a terrain with forests and woodlands. Nevertheless, this methodology is general-purpose and not site-dependent, and in the future, its performance will be studied in other types of terrain and wind farms.