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Located in East Africa, Uganda is one of the most economically deprived countries that is likely to be dramatically affected by climate change. Over 50% of Ugandan families live in single-roomed overcrowded properties and over 60% of the country’s urban population live in slums. Moreover, the gradual shift towards relatively modern and low thermal resistance building materials, in addition to imminent thermal discomfort due to global warming, may considerably affect the health and wellbeing of low-income people, the majority of whom live in low quality homes with very little or no access to basic amenities. This paper evaluates the effects of various construction methods as well as refurbishment strategies on thermal comfort in low-income houses in Uganda. It is aimed at helping low-income populations adapt to climate changes by developing simple, effective and affordable refurbishment strategies that could easily be applied to existing buildings. Dynamic thermal simulations are conducted in EnergyPlus. The adaptive model defined in BS EN 15251 and CIBSE TM52 is used to evaluate the risk and extent of thermal discomfort. Roofing methods/materials are found to be the key factor in reducing/increasing the risk of overheating. According to the results, roof insulation, painting the roof with low solar absorptance materials and inclusion of false ceilings are, respectively, the most effective and practical refurbishment strategies in terms of improving thermal comfort in low-income houses in Uganda. All refurbishment strategies helped to pass Criterion 3 of CIBSE TM52, as an indicator of “future climate scenarios”, making low-income houses/populations more climate resilient.

This work addresses the problem of energy consumption time series forecasting. In our approach, a set of time series containing energy consumption data is used to train a single, parameterised prediction model that can be used to predict future values for all the input time series. As a result, the proposed method is able to learn the common behaviour of all time series in the set (i.e., a fingerprint) and use this knowledge to perform the prediction task, and to explain this common behaviour as an algebraic formula. To that end, we use symbolic regression methods trained with both single- and multi-objective algorithms. Experimental results validate this approach to learn and model shared properties of different time series, which can then be used to obtain a generalised regression model encapsulating the global behaviour of different energy consumption time series.

China is a large agricultural country with a high level of agricultural carbon emissions. Whether market prices can be used in agricultural production as a means of agricultural carbon emissions reduction is of great significance to improve the allocation of agricultural production factors and expand large-scale production. This paper applies an autoregressive distributed lag–pooled mean group(ARDL–PMG) model to evaluate the relationship between agricultural production factor prices, food consumption prices, and agricultural carbon emissions, using Chinese provincial panel data from 1994 to 2018. The results show that agricultural carbon emissions and agricultural production factor prices show environmental Kuznets curve (EKC) characteristics; agricultural carbon emissions and food prices show a U-shaped curve; and agricultural production factors are positively correlated with food price in both directions in the long-term. The results of Granger causality tests show that price is the cause of agricultural carbon emissions; the price of agricultural production factors and the price of food consumption are mutually causal. Such results have implications for price, agriculture, and environmental policies. The analysis implies that the market price can be applied to agricultural carbon reduction, which will help policymakers to implement effective price policies in order to reduce agricultural carbon emissions. One implication is that promoting the marketization of agricultural production factors and reducing price distortions will be conducive to carbon emissions reduction in agriculture, which in turn will increase food consumption prices. Therefore, subsidies are needed at the consumption end, which will eventually achieve further carbon emissions reduction at the production and consumption ends.

Since the Paris Agreement in COP21, many countries around the world, including Ghana and Thailand, have established a Nationally Determined Contribution (NDC) to reduce greenhouse gas (GHG) emissions, with first update recently in COP26. With Ghana’s ongoing effort at COP26 to change its baseline to 2019, this study established a detailed Ghana vehicle ownership model with necessary transport parameters to construct an energy demand model to provide insight for reducing GHG emission contributions from road transport through biofuel (both bioethanol and biodiesel) potential by recourse to a Low Emission Analysis Platform (LEAP), with two scenarios of development from Thailand’s best practice for policy recommendation, which are alternative (ALT), with up to E20/B20, and extreme (EXT), with up to E85/B50, for new vehicles. In each case, energy demand and GHG emissions were analyzed from detailed data on Ghana’s transport sector to show potential benefit from biofuel usages. From Ghana’s transport sector contribution to NDC, 8.4% and 11.1% of GHG emission reduction in 2030 can be achieved with a 0.13% and 0.27% additional arable land requirement from ALT and EXT scenarios. Policy recommendation and implication were also discussed.

Energy efficiency is, in principle, a simple idea: an output of human value, for example, vehicle-km traveled, divided by the needed input energy. Efficiency improvements are regarded by many as an important means of mitigating not only climate change, but also other environmental problems. Accordingly, many countries have efficiency ratings for appliances and efficiency standards for road vehicles. Despite the vast number of articles published on energy efficiency, few question whether it is a useful or accurate measure in its present form. This review addresses this lack, by a critical review of the literature, not only in energy efficiency, but in other areas of research, such as ‘energy services’, that can help broaden the scope of this idea, both geographically and conceptually. These shortcomings are illustrated in case studies of road passenger transport and buildings. The main findings are that energy efficiency inevitably has an ethical dimension, that feedbacks are more widespread than generally considered, and that conventional efficiency measures omit important energy input items, particularly those concerned with mining of the materials needed for renewable energy plants. Finally, the key results of this review are summarized, and its limitations are discussed, as is the future research needed to overcome these shortcomings.

Solar PV and battery energy storage (BES) costs for domestic consumers are constantly diminishing. On top of this, the end of the Feed-in-Tariff programme has significantly increased interest in combined PV + BES systems. In this paper, we explore the economics of domestic PV + BES systems, extending the current literature on the topic via the use of a large smart meter dataset and a demographic comparison. Predictably, time-varying tariffs and higher electricity prices generally strengthen the economic arguments for PV + BES systems, however our consumption data yields a wide range of Net Present Values for different consumers. In terms of demographics, we find that batteries are more favourable for more affluent households due to their larger consumption levels, though profitability becomes more uniform if the batteries are tailored to individual households. This is an important point for policy, since it indicates that if PV + BES systems become widely profitable this is unlikely to help the financial situation of lower-income households.

In this paper, the immiscible water-alternating-CO2 flooding process at the LH11-1 oilfield, offshore Guangdong Province, was firstly evaluated using full-field reservoir simulation models. Based on a 3D geological model and oil production history, 16 scenarios of water-alternating-CO2 injection operations with different water alternating gas (WAG) ratios and slug sizes, as well as continuous CO2 injection (Con-CO2) and primary depletion production (No-CO2) scenarios, have been simulated spanning 20 years. The results represent a significant improvement in oil recovery by CO2 WAG over both Con-CO2 and No-CO2 scenarios. The WAG ratio and slug size of water affect the efficiency of oil recovery and CO2 injection. The optimum operations are those with WAG ratios lower than 1:2, which have the higher ultimate oil recovery factor of 24%. Although WAG reduced the CO2 injection volume, the CO2 storage efficiency is still high, more than 84% of the injected CO2 was sequestered in the reservoir. Results indicate that the immiscible water-alternating-CO2 processes can be optimized to improve significantly the performance of pressure maintenance and oil recovery in offshore reef heavy-oil reservoirs significantly. The simulation results suggest that the LH11-1 field is a good candidate site for immiscible CO2 enhanced oil recovery and storage for the Guangdong carbon capture, utilization and storage (GDCCUS) project.