Editorial: Towards Net-Zero ‘Greenhouse Gas’ Emissions by 2050
Notice bibliographique
Résumé
In 2023 the world experienced record-breaking temperatures and extreme weather events (UNEP, 2023). These have included dramatic floods in Libya that killed more than 6000 people (Darby et al., 2024) and damaged critical infrastructure in that already civil-war-ravaged country, and severe wildfires in Canada that burned an area the size of Syria. Global warming (sometimes-called ‘global heating’) is, at least in part, contributing to such events (Clarke et al., 2022; IPCC, 2023; Waskow et al.). Trace gases in the upper atmosphere, and clouds nearer the surface, then reflect a proportion of the Sun's rays back out into space. The land and oceans absorb the solar radiation that does get through the atmospheric blanket. These warmer surfaces in turn re-radiate infrared, or long-wave, thermal radiation back to the atmosphere. Some of the trace gases then absorb, or trap, this heat in an analogous manner to the action of glass in a greenhouse. Consequently, these radiation-absorbing gases are termed ‘greenhouse gases’ [GHGs] (Darby et al., 2024). Human activities since 1950, primarily those that involve burning fossil fuels, have led to dramatic increases in atmospheric concentrations of carbon dioxide [CO2] (Cohen et al., 2022; Hammond, 2022; IPCC, 2023); the dominant GHG with an atmospheric residence time of 50–200 years; with 20–60% remaining airborne for a thousand years or longer (Archer and Brovkin, 2008). These CO2 concentrations have risen from 330 parts per million (ppm) in 1975 to about 420 ppm in 2023. Such changes in atmospheric concentrations of GHGs affect the energy balance of the global climate system, giving rise to higher surface air temperatures, and the resulting extreme weather events (CAT, 2023; Hammond, 2022; IPCC, 2023; Waskow et al.). The Intergovernmental Panel on Climate Change [IPCC], in its most recent [2023] scientific assessment, asserts that human activities, principally through GHG emissions, have ‘unequivocally’ caused observed global warming since the mid-20th Century (IPCC, 2023), with mean global surface temperature reaching 1.1°C above 1850–1900 levels in the last decade. Global mean sea levels are continuing to rise, with the extent of Arctic and Antarctic sea-ice well below average.This issue of the Institution of Civil Engineers’ [ICE] proceedings journal Energy is being published during a year when more than 40% of the world's population is eligible to vote in democratic elections. India, with nearly 970 million eligible voters emits some 3.0 Gt of GHG emissions per annum [pa] or 7.3% of the global total (Darby et al., 2024). The ruling party – the Bharatiya Janata Party [BJP] led by the Prime Minister Narendra Modi – lost its majority in the June elections and now needs to govern in a coalition. This is significant in that it is a major player in climate change negotiations. On the very last day of the Glasgow Climate Summit [COP26] in Glasgow in late 2021, the G77 group of developing countries plus China [effectively guided by India's then environment minister, Bhupender Yadav] objected to the wording ‘phase-out coal’ in the final document and, after tense ‘huddles’, it was replaced by the expression ‘phase-down coal’ (Cohen et al., 2022). The then BJP-administered Indian Government subsequently pledged only to achieve net-zero emissions [i.e., carbon neutrality’] by 2070, in contrast to the internationally agreed target of net-zero by 2050, and thereby slowed progress in mitigating world GHG emissions (Darby et al., 2024). The European Union [EU-27] also went to the polls in June, with more than 400 million people eligible to vote. It emits around 2.8 Gt pa of GHGs or 6.7% of the global total. Both the ‘hard right’ and ‘far left’ groups in the European Parliament made gains at the expense of the centre, although the centre-right [Christian-democrat and conservative parties of the European People's Party {EPP} group] still has the largest block. This shift may lead to a ‘watering down’ of the flagship EU Green Deal (EP, 2020), as a number of parties on the ‘populist right’ have been critical of the project.In the UK, with just about 47 million eligible voters, a snap General Election will take place on 4 July. It is an influential climate change player, that hosted the 2021 Glasgow Climate Summit [COP26], in partnership with Italy, notwithstanding the fact that Britain only emits some 0.5 Gt of GHGs or around 1% of the world total annually (Hammond, 2022). Of the two major political parties, the governing Conservative Party [led by the Prime Minister Rishi Sunak] has recently been diluting its commitments to climate change mitigation, whilst the opposition Labour Party [headed by Keir Stammer] has traditionally been more enthusiastic in terms of meeting a net-zero target by 2050 with an aspiration to make ‘Britain a clean energy superpower’. At the time of writing, opinion polls give Labour over twice the potential vote share of its Conservative competitor. Finally, the United States of America [USA] – having roughly 244 million eligible voters – has both Presidential and Congressional elections scheduled for early November 2024. It is another major GHG emitter – second only to China – that discharges some 4.7 Gt pa or 11.2% of the global total, so that changes in the Executive and Legislature could damage US climate change commitments. The current Democrat President Joe Biden, for example, has been active in supporting the global net-zero GHG emissions target for 2050 alongside renewable energy developments, whereas his challenger, former Republican President Donald Trump, is likely to push the USA further away from a 2050 net-zero pathway as he demonstrated in his first term.The ICE Energy journal now has a ‘strapline’ of ‘energy transitions in the era of climate change’, and that reflects our recognition of the importance of the mitigation of GHG emissions. Energy systems are at the heart of this agenda, and thus our interest in the outcome of COP28 (Darby et al., 2024). It took place after a Global Stocktake [GST] of progress made by signatories to the 2015 Paris Agreement on climate change (UNFCCC, 2015) that aimed to keep temperatures ‘well below 2°C above pre-industrial levels and to pursue efforts to limit the temperature increase to 1.5°C above pre-industrial levels’ (Burnett, 2023; Cohen et al., 2022; IPCC, 2023). The achievements at COP28 were mixed, and disappointed many delegations [including those from the climate-vulnerable countries at high risk from extreme weather events]. The results of the GST identified a significant GHG emissions gap between actions needed to ‘keep 1.5°C alive’ and those identified in the GST that were being carried out (CAT, 2023; UNEP, 2023; Darby et al., 2024). National GHG emissions reduction targets submitted prior to the summit would result in a 2.4°C global warming by the end of the century; a long way short of the 1.5°C aspiration. However, if further pledges, for example, by India [of achieving net-zero emissions by 2070] were fully achieved, then estimated global warming would peak at around 1.9°C before falling to 1.8°C by 2100 (CAT, 2023)]. The Parties at the COP28 UN Dubai Climate Summit did agree to ‘transition away from fossil fuels’ in order to reach ‘net-zero’ anthropogenic GHG emissions [or carbon neutrality] by 2050, and to triple renewable energy capacity and double energy efficiency by 2030. But many regretted the absence of references to the phase-out or phase-down of fossil fuels (Darby et al., 2024).The present issue of the ICE Energy journal reflects on some of the issues and technologies needed for the energy transition towards a low-carbon future. In this regard it includes papers related to the energy demand-side (building energy performance modelling), the power sector supply-side [solar energy and wind power], and the assessment of economic ‘externalities’ associated with the power system overall. The first paper by Siwach et al. (2024) concerns the use of an AI-based machine-learning approach to the investigation of building energy performance. Three variants of artificial neural network (ANN) models were employed to determine the output parameters of heating and cooling loads against eight input characteristics of a benchmark test case derived from an earlier UK study by Tsanas and Xifara (2012), based on a statistical machine-learning framework. The latter study indicated the efficacy of using a classification and regression tree (CART) method based on the so-called ‘random forest’ (RF) statistical method for complex applications. Siwach et al. (2024) follow this framework and the associated benchmark [large, detached residential] building quite closely, albeit via their alternative ANN models. They examined 96 conditions by varying glazing area and its distribution. That suggests a scope for significant improvement in the ‘energy efficiency’ of buildings. They found a maximum saving of some 20% in cooling load under specific glazing arrangements.On the supply-side, Zhang et al. (2024) have proposed a segmented specular reflection solar concentrator for power generation. Many mirrors are arranged such that all the reflected rays fall on the cylindrical focal point. Theoretical spectral analysis showed that the geometric concentrating ratio of the concentrator exhibits a linear relationship to the number of mirrors, while the area utilisation ratio is a function of the ratio between the span of mirrors and the height of receivers. A companion photothermal experimental study, utilising a double-tube heat collector, found that the volume of oil flow in the pipeline had basically no influence on photothermal conversion efficiency of the concentrator system. Theoretical analysis of the conversion efficiency was only a little higher than the experimental values over the measured temperature range. Small glass mirrors were employed for the reflecting surface, which resulted in a relatively low cost and can be installed more conveniently. A second supply-side paper by Roga et al. (2024) concerns an assessment of the wind energy potential in Central India, employing data from Dr. Babasaheb Ambedkar International Airport in the ‘Orange City’ of Nagpur (within the state of Maharashtra). They used the ‘Windographer’ proprietary software to estimate wind speeds, wind rosettes, and wind power densities based on the Weibull wind distribution. The site exhibited low wind speeds, as might be expected at a site that features gently rolling hills positioned between the Bay of Bengal and the Indian interior. However, the authors argue that it could prove suitable for smaller-scale applications such as rooftop wind turbines for charging and low-wattage devices. They suggest that it might facilitate wind-based hybrid systems and standalone installations in remote areas, thereby reducing electricity costs and enhancing resilience.The last paper in this issue of the ICE Energy journal relates to the challenge of incorporating economic ‘externalities’ into the optimisation of power system expansion plans. They are caused by organisations [typically firms] that do not internalise the indirect costs of, or the benefits from, their operations that lie outside of market transactions. These often relate to environmental costs arising from GHG emissions and other pollutants. Wyatt (2024) suggests that such externalities, together with budget constraints and demand-management measures, can now be explicitly incorporated into project evaluation. He argues that the supply-side has become more complicated with the availability of wind, solar and biomass plants in addition to conventional thermal and hydro plants. Wyatt uses the Vietnamese power system as an example, in order to demonstrate the feasibility of evaluating externalities when mathematically optimising integrated generation and transmission system expansion plans. The ‘Capricorn’ proprietary expansion planning software was employed in this context. Supply mixes under six optimisations are presented via the relevant generation plant outputs and transmission line flows. One of these assessed the GHG emission constraints under Vietnam's ‘higher obligations’ under the 2015 Paris Agreement on climate change (UNFCCC, 2015).This journal will continue to champion technological and behavioural science developments that can underpin a low-carbon energy transition in support of net-zero GHG emissions reduction by 2050. ‘Ahead of print’ the reader will find further contributions on energy conservation and renewable energy technologies, such as other studies of the energy performance of residential buildings, distributed generation and electric vehicle load management, the design of wave energy converters, and tidal range electricity generation. Already in the pipeline are Themed Issues on ‘Skills for Energy Transitions’ and ‘Medium and Long Duration Energy Storage’. Thus, the journal will persist in its support of the energy-related climate change challenges identified at successive UN Climate Change Conferences of the Parties, and there potential solutions (Cohen et al., 2022; Darby et al., 2024). ‘Engineering net-zero’ will remain the focus of this journal, although the achievement of UN climate change priorities will obviously depend on geopolitics as reflected, for example, in this year's elections within democracies around the world.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».