• Energy Research

AbstractGlobal greenhouse gas (GHG) emissions can be traced to five economic sectors: energy, industry, buildings, transport and AFOLU (agriculture, forestry and other land uses). In this topical review, we synthesise the literature to explain recent trends in global and regional emissions in each of these sectors. To contextualise our review, we present estimates of GHG emissions trends by sector from 1990 to 2018, describing the major sources of emissions growth, stability and decline across ten global regions. Overall, the literature and data emphasise that progress towards reducing GHG emissions has been limited. The prominent global pattern is a continuation of underlying drivers with few signs of emerging limits to demand, nor of a deep shift towards the delivery of low and zero carbon services across sectors. We observe a moderate decarbonisation of energy systems in Europe and North America, driven by fuel switching and the increasing penetration of renewables. By contrast, in rapidly industrialising regions, fossil-based energy systems have continuously expanded, only very recently slowing down in their growth. Strong demand for materials, floor area, energy services and travel have driven emissions growth in the industry, buildings and transport sectors, particularly in Eastern Asia, Southern Asia and South-East Asia. An expansion of agriculture into carbon-dense tropical forest areas has driven recent increases in AFOLU emissions in Latin America, South-East Asia and Africa. Identifying, understanding, and tackling the most persistent and climate-damaging trends across sectors is a fundamental concern for research and policy as humanity treads deeper into the Anthropocene.

AbstractGlobal greenhouse gas (GHG) emissions can be traced to five economic sectors: energy, industry, buildings, transport and AFOLU (agriculture, forestry and other land uses). In this topical review, we synthesise the literature to explain recent trends in global and regional emissions in each of these sectors. To contextualise our review, we present estimates of GHG emissions trends by sector from 1990 to 2018, describing the major sources of emissions growth, stability and decline across ten global regions. Overall, the literature and data emphasise that progress towards reducing GHG emissions has been limited. The prominent global pattern is a continuation of underlying drivers with few signs of emerging limits to demand, nor of a deep shift towards the delivery of low and zero carbon services across sectors. We observe a moderate decarbonisation of energy systems in Europe and North America, driven by fuel switching and the increasing penetration of renewables. By contrast, in rapidly industrialising regions, fossil-based energy systems have continuously expanded, only very recently slowing down in their growth. Strong demand for materials, floor area, energy services and travel have driven emissions growth in the industry, buildings and transport sectors, particularly in Eastern Asia, Southern Asia and South-East Asia. An expansion of agriculture into carbon-dense tropical forest areas has driven recent increases in AFOLU emissions in Latin America, South-East Asia and Africa. Identifying, understanding, and tackling the most persistent and climate-damaging trends across sectors is a fundamental concern for research and policy as humanity treads deeper into the Anthropocene.

Abstract Since 2010, over 700,000 small-scale solar photovoltaic (PV) systems have been installed by households in Great Britain and registered under the feed-in tariff (FiT) scheme. This paper introduces a new agent-based model which simulates this adoption by considering decision-making of individual households based on household income, social network, total capital cost of the PV system, and the payback period of the investment, where the final factor takes into account the economic effect of FiTs. After calibration using Approximate Bayesian Computation, the model successfully simulates observed cumulative and average capacity installed over the period 2010–2016 using historically accurate FiTs; setting different tariffs allows investigation of alternative policy scenarios. Model results show that using simple cost control measures, more installation by October 2016 could have been achieved at lower subsidy cost. The total cost of supporting capacity installed during the period 2010–2016, totalling 2.4 GW, is predicted to be £14 billion, and costs to consumers significantly exceed predictions. The model is further used to project capacity installed up to 2022 for several PV cost, electricity price, and FiT policy scenarios, showing that current tariffs are too low to significantly impact adoption, and falling PV costs are the most important driver of installation.

Abstract Since 2010, over 700,000 small-scale solar photovoltaic (PV) systems have been installed by households in Great Britain and registered under the feed-in tariff (FiT) scheme. This paper introduces a new agent-based model which simulates this adoption by considering decision-making of individual households based on household income, social network, total capital cost of the PV system, and the payback period of the investment, where the final factor takes into account the economic effect of FiTs. After calibration using Approximate Bayesian Computation, the model successfully simulates observed cumulative and average capacity installed over the period 2010–2016 using historically accurate FiTs; setting different tariffs allows investigation of alternative policy scenarios. Model results show that using simple cost control measures, more installation by October 2016 could have been achieved at lower subsidy cost. The total cost of supporting capacity installed during the period 2010–2016, totalling 2.4 GW, is predicted to be £14 billion, and costs to consumers significantly exceed predictions. The model is further used to project capacity installed up to 2022 for several PV cost, electricity price, and FiT policy scenarios, showing that current tariffs are too low to significantly impact adoption, and falling PV costs are the most important driver of installation.

The use of biomass to produce biofuels can lead to both direct and indirect Land Use Change (LUC). While the causes underlying LUCs are complex their quantification is a scientific challenge that h...