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This study argues that improving material efficiency should be seriously considered as a mitigation policy objective alongside energy efficiency in order to meet the net-zero carbon targets and climate mitigation policies should be designed at the industry level based on the industry-specific needs and peculiarities. Therefore, by highlighting the emerging role of material use in greenhouse gas emission mitigation, this paper aims to investigate the degree of substitution between inputs for South Korea at the industry level, with a particular focus on substitution/complementarity possibilities between energy and materials. To this end, we make use of the Asia KLEMS database covering a wide range of industries and estimate translog cost share equations using Zellner’s seemingly unrelated regression to compute price elasticities. We consider two models derived from translog cost function, i.e. the static and dynamic. The empirical results provide important insights about how industry-specific material demand reduction policies could help to reduce emissions and improve resource efficiency when integrated with energy efficiency.

In assessing energy security, risk is recognised as being important. However, it is rarely assessed in a transparent quantitative way. To address this, we decompose the global energy system into 27 generic fuel supply chains and six sequential process stages. One of these fuels – ‘Demand Reduction’ – is conceived as ‘negative fuel’ to satisfy the supply/demand balance. We use 7 categories of risk and estimate the likelihood and impact for each cause of risk for each fuel and each process stage – demonstrating the tractability of creating a risk matrix for the whole energy system. Impacts of risk events are evaluated using the triple-bottom-line methodology, allowing for system resilience. This direct assessment of risk events is based on published data, interpretation and comment (with risk seldom directly articulated), and eliminates the need for arbitrary weighting of individual risks in the overall assessment. In a UK case study, 19 of the fuel categories were found to be relevant and those with the highest overall risk were a group of fossil fuels and nuclear fission. The least risky fuels were renewables, including solar (electric and thermal), wind, and hydro sources. Cost reduction alone was shown to be insufficient to deliver an efficient and more environmentally benign energy system. The UK case study demonstrates the need for governments to consider more closely policy relating to long-term energy security and identifies key elements of risk hindering the low-carbon transition.

The illegal trade in wildlife products is a major driver of the global biodiversity crisis. Trade in wildlife products is driven by consumer demand; however, consumer's motivations are often poorly understood. In this study, we use mixed social science approaches to understand the motivations driving consumers of bear products for medicine in Vietnam, and of traditional medicine practitioners who may be influencing consumers. In addition, we provide current information about the ways bear products are used in the two largest cities of Vietnam: Hanoi and Ho Chi Minh City. We found that bear products are still used widely in Vietnam, despite their use being prohibited since 2006. We directly estimated use at 45% of the sample of consumers for Hanoi, and 18% for consumers in Ho Chi Minh City. However, bear products are used differently between the two cities, with Hanoians more likely to take bear medicine products to treat an ailment, versus Ho Chi Minh City, where it is taken as a daily tonic. We also found that some traditional medicine practitioners in Vietnam are continuing to prescribe bear products, despite medicinal prescription of bear bile being made illegal, and availability of traditional medicine herbal alternatives. Generally, use of bear products appears to still be widely acceptable in the country, indicating a need for changing the social norms of bear product consumption. The insights gathered here will be beneficial to conservation managers working in Vietnam and throughout the Southeast Asia region, and will be particularly informative for developing and implementing demand reduction campaigns.

Demand management programs help utilities to reduce the load demand during high wholesale electricity price or system peak periods to maintain the system reliability and operational security. The challenge here is to properly address the customers’ inconvenience to encourage them to participate and meanwhile satisfy the required demand reduction efficiently. To this end, we propose a new incentive-based demand management scheme for schedulable appliances in a residential community. The proposed scheme is designed to provide scalability to the system. Different from existing literature, a new compensation scheme is adopted for the shifting of task-based appliances based on the level of inconvenience. The potentials of the schedulable appliances to contribute during the demand reduction event are assessed with a modified demand response potential. In this approach, the utility sends command and consumption limit to several controllers to perform the energy optimization. Participating customers are benefited with financial compensation along with minimized privacy concerns in the proposed centralized approach. Considering different demand reduction events within a month, the electricity bill with the proposed scheme is compared with the electricity bill from time-of-use (TOU) tariff-based optimization scheme and also with the base case (i.e., without any energy optimization scheme). Comparison of monthly electricity costs shows that the proposed scheme can save 11.3% on average more than the base case, and also the proposed scheme can save 6.2% on average more than the TOU tariff-based optimization.

Demand response programs are becoming an integral part of the power system, helping create a closer alignment between the electrical service providers and customers. The research described in this paper uses the residential demand response (DR) program during a peak demand event to determine the demand reduction capacity as a virtual storage (VS). As in the marketing business, identifying target customers is vital in the DR program, thus making it more efficient and productive. Additionally, peak load events are very critical in the power system; therefore, it is essential to model an effective demand response program. This paper uses predictive analytics to estimate the level of residential participation in a DR program, and thus the load reduction capacity available, during peak load events. Also derives the mathematical modeling of the demand reduction capacity of a demand response program as a virtual storage system and optimizes it using genetic algorithm technique.

In its 2016 Heating and Cooling Strategy, the European Commission (EC) highlighted the strategic importance of heating demand for the energy demand reduction, and further noted that District Heat Networks (DHN) can play an important role in decarbonising this sector. This study applied a thermal atlas approach to map the potential for district heat networks in Switzerland. It extended existing methods with a novel approach to estimating linear thermal demand density in DHN at a national scale. DHN potential for current-generation high temperature networks as well as cutting-edge low temperature networks were compared for current building space heating and hot water demand as well as for two demand reduction scenarios. The method was tested by comparing its results to those of a local engineering study conducted for a Swiss municipality (Brig-Glis). The potential percentage of demand supplied by high temperature DHN was shown to decrease from 66% to 41% with energy saving while the potential for low temperature systems increased significantly from 2.1% to 42%. The percentage of heat demand covered by heat networks decreases less than the percentage of buildings covered, reflecting the strength of heat networks for supplying large fractions of thermal demand in geographically confined areas.

Achieving further reductions in building electricity usage requires a detailed characterization of electricity consumption in homes. Understanding drivers of consumption can inform strategies for promoting conservation and efficiency. While there exist numerous approaches for modeling building energy demand, the use of regularization methods in statistical models can address challenges inherent to building energy modeling while also enabling more accurate predictions and better identification of variables that influence consumption. This paper applies five regularization techniques to regression models of original survey and electricity consumption data for more than one thousand households in California. It finds that of these, elastic net and two extensions of the lasso—group lasso and adaptive lasso—outperform other approaches in terms of prediction accuracy and model interpretability. These findings contribute to methodological approaches for modeling energy consumption in buildings as well as to our understanding of key drivers of consumption. The paper shows that while structural factors predominate in explaining annual electricity consumption patterns, habitual actions taken to save energy in the home are important for reducing consumption while pro-environmental attitudes and energy literacy are not. Implications for improving building energy modeling and for informing demand reduction strategies are discussed in the context of the low-carbon transition.

The US switched its China and Taiwan anti-dumping duties to uniform tariffs on all imports of solar panels in January 2018. This research uses an Equilibrium Displacement Model (EDM) to determine the relative effects of these two tariffs on domestic consumer and producer welfare, employment, government revenue, and environmental costs. The results indicate that uniform tariffs are more effective than the specific-country tariffs because of higher welfare for domestic manufacturers and higher government revenue. However, the uniform tariffs hurt American consumers, employees, and the environment more than the other because of higher domestic price and larger domestic demand reduction. Therefore, if a policy aims to create more jobs and have less effect on consumers and the environment, a specific-country tariff might be preferable. However, uniform tariffs are better if the administration intends to support domestic manufacturers and increase government revenue.

Greenhouse gas emission (GHGE) taxes on food products have recently been proposed as means to help reduce agricultural emissions. Numerous authors have calculated potential GHGE reductions in case such a tax was implemented in certain countries or regions. They did however assume a reduced production of GHGE-intense foods equal to the decline in demand induced by the tax. This omits however possible increases of net-exports that might offset such a demand reduction. Herein, the market dynamic behind this so-called “emission leakage” is explained and its effect quantified for a greenhouse gas tax in the European Union. We use the European Forest and Agricultural Sector Optimization Model for the quantitative analysis and simulate a greenhouse gas tax on all food products, based on their individual emission levels. The partial equilibrium model covers all world regions and hence the tax's effects on international trade of agricultural commodities can be examined. It was found that 43% of the greenhouse gas reduction indicted by a domestic consumption reduction is lost through emission leakage. This already includes the mitigating effects of a production shift from inefficient to efficient producers that is another consequence of increased exports from the European Union. A greenhouse gas emission tax on food products is hence much less efficient than previously proposed, if it is not introduced globally or trade is not restricted.

Demand response (DR) is a set of demand-side activities to reduce or shift electricity use to improve the electric grid efficiency and reliability. Shutting down part of operating chillers directly in central air-conditioning systems of buildings to meet the urgent demand reduction needs of power grids has received increasing attention recently. A model predictive control strategy is developed for optimizing the operation of a central air-conditioning system integrated with an active cold storage during such fast demand response events. The primary objective of the proposed control strategy is to maximize the power reduction with a profile pattern preferred by power grids as well as to maintain the indoor air temperature within an acceptable level during fast DR period. The MPC controller determines the control outputs of chiller power demand and active storage discharge rate based on the discrete-time state space building thermal response model. Test results show that expected profile pattern (i.e., maximum and stable) of power reduction was achieved by the proposed MPC strategy during fast DR events. Meanwhile, the indoor air temperature was maintained within the acceptable range.

Residential air-conditioner loads are known to be a key driver of summer demand peaks for some electricity industries. Such demand peaks occur infrequently but have potentially severe implications for network and peaking generation plant investment, and hence electricity prices for end users. Detailed assessments of air-conditioner peak demand have, however, been hampered by limited residential consumption data, and even when interval meter data is available, the complexities of other household loads and behavior. This study utilizes data from a significant appliance level consumption monitoring project in Sydney, Australia, to analyze the actual contribution of air-conditioners to regional summer demand peaks. K-means clustering is used to characterize air-conditioner load profiles across a large number of households at times of these overall demand peaks. These clustered air-conditioning load profiles, combined with a number of possible load control strategies, are then used to estimate possible peak load reductions from air-conditioner demand response across the Australian State of New South Wales. Our findings suggest that residential air-conditioning presents a significant demand response opportunity, approaching perhaps 9% of total peak demand in some circumstances.

Air-conditioning systems in commercial buildings play a vital role in the demand respond (DR) of cities. At present, many researches are focused on zone temperature reset strategies which are used frequently in the commercial buildings of the United States. However, this strategy requires the support of the energy management and control system (EMCS), which is not suitable for most commercial buildings in China. This paper focuses on the DR effect of the chiller-side control strategies in Chinese commercial buildings. Taking an office building as the research object, the coupling thermal inertial model of the building and the air conditioning system was built; based on the coupling model, the demand reduction effects of the typical chiller-side DR control strategies, including intermittent shutdown, chiller quantity reduction and chilled water temperature increase, were simulated; and the characteristics and applicability of these strategies were pointed out.

Recognition is growing that gambling, although highly profitable for corporations and governments, is a source of serious and unevenly distributed harm. This recognition has led to demands for public health strategies at the local, national, and international levels. We aimed to identify review-level evidence for interventions to address or prevent gambling-related harms and explore policy implications, using stakeholder consultation to assess the evidence base, identify gaps, and suggest key research questions. We opted for a systematic mapping review and narrative synthesis for all forms of gambling in any setting. We included participants from the whole population, identified gamblers including self-defined, and specific populations at risk (eg, children and young people). We included all outcome measures relating to prevention or treatment of gambling-related harms that were reported by review authors. After duplication, the searches generated 1080 records. Of 43 potential papers, 13 were excluded at the full paper stage and 30 papers were included in the Review. We identified whole-population preventive interventions, such as demand reduction (n=3) and supply reduction (n=4) interventions, and targeted treatment interventions for individuals addicted to gambling, such as therapeutic (n=12), pharmacological (n=5), and self-help or mutual support (n=4) interventions. We also reviewed studies (n=2) comparing these approaches. Interventions to screen, identify, and support individuals at risk of gambling-related harms and interventions to support ongoing recovery and prevent relapse for individuals with a gambling addiction were not represented in the review-level evidence. A public health approach suggests that there are opportunities to reduce gambling-related harms by intervening across the whole gambling pathway, from regulation of access to gambling to screening for individuals at risk and services for individuals with an identified gambling problem. The dearth of evidence for some interventions means that implementation must be accompanied by robust evaluation.

Existing international environmental institutions curb fossil fuels by rewarding countries for reducing demand and expanding substitutes. This paper argues that it would be beneficial to create new institutions that would reward countries for reducing their fossil fuel supply. Assuming complete information, I prove a Price Preservation Lemma: For any given budget, the optimal way to split the budget between the three approaches (rewarding supply reduction, demand reduction and substitute expansion) leaves the world market price of the fossil fuel unchanged. In a dynamic setting, this result holds under full commitment for any given intertemporal budget. Using this Lemma, I show that the optimum cannot be implemented by relying on the supply side entirely on a deposit purchase fund. The results suggest that it would be valuable to also create funds rewarding countries for taxing fossil fuel extraction in addition to creating a deposit purchase fund.

Taking advantage of the thermal inertia of a building to shift the cooling load during a demand response (DR) period is an important strategy for commercial buildings. However, researchers have consistently ignored the effects of the thermal inertia of air-conditioning systems on the DR potential of buildings. This study focused on the coupling effect of a building and an air-conditioning system. This paper describes the construction of a coupled thermal inertia model of a building and an air-conditioning system, which was validated experimentally; analyzes the accuracy of the thermal inertia of a building solely, as well as the accuracy of both the building and air-conditioning system together by comparing the model and experiment results; and quantifies the DR contributions of the thermal inertia of both the building and the air-conditioning system. A considerable demand reduction effect of the thermal inertia of the air-conditioning system was found, and the results of coupling the thermal inertia of both the building and air-conditioning system showed greater accuracy.

Peak load management strategies are useful to commercial building operators for saving on energy costs and also to electricity grid operators for helping to balance power supply and demand. Peak load demand reduction can be achieved through demand-side management that facilitates the planning and implementation of demand response strategies and maintains an acceptable indoor environment. This paper tracks the development of peak load management in the literature and presents an overview that combines the following demand-side management domains: demand response programs, strategies and techniques, load forecasting models, and occupant comfort. Demand response programs are presented through examples from different jurisdictions that show the application of price and incentive-based pricing mechanisms. Demand response strategies and techniques suitable for peak load reduction in commercial buildings are identified and explained through illustrative examples using data from an office building in Ottawa, Canada. Load forecasting models commonly used to predict a building's load profile are discussed and compared regarding their prediction accuracy and data requirement. An overview of the impact of implementing demand response strategies on the indoor environment is presented with recommendations to optimize peak load reduction while maintaining building occupants' comfort. This overview highlights the relationship between demand-side management domains through illustrative examples, and provides a practical reference that facility managers and building operators can use for implementing their demand response strategies with minimal impact on the indoor environment.

Shutting down part of operating chillers directly in central air-conditioning systems of buildings to meet the urgent demand reduction needs of power grids has received increasing attention recently. However, due to limited cooling supply during above demand response (DR) events, the indoor air temperature and particularly relative humidity would often increase to unacceptable levels, resulting in the failures of DR controls. Considering the restriction on power use during DR events, rational use of limited cooling supply turns out to be the inevitable choice. The feedback control strategies commonly-used today cannot properly deal with the environment and system control issues under limited cooling supply during DR events. However, no study on this problem can be found in the research literature. As the first effort, two control strategies (i.e., optimal and near-optimal) are developed to address the environment control issues (concerning both indoor temperature and humidity controls) under a pre-determined power limiting threshold during DR events. The optimal control strategy optimizes the air flow set-points of individual AHUs (air handling units) using model-based prediction and genetic algorithm to achieve the best possible indoor environment control. The near-optimal control strategy approaches such best environment control using a simple empirical method. Case studies are conducted and the results show that the air flow settings have significant impacts on the indoor environment controlled under limited cooling supply. Both control strategies can achieve significant improvements in the indoor temperature and humidity controls as well as significant fan power saving.

The idea that energy is not consumed for its own sake but for the services that it provides has become axiomatic. However, the implications are not worked through into energy policy nor into most analyses of energy demand. Instead, energy service demand is usually isolated from its dynamic and varied socio-cultural basis, rendering it inappropriately static and neglecting the core quality of usefulness that definitions of ‘energy service’ share. To address these limitations, this paper revisits and extends a sociological conceptualisation of services, referred to here as meta-services. These are composite and cross-cutting formations of convention, expectation and experience and the means of achieving them. Meta-services are more-than-energy services and are shaped not only through energy consumption, provision and governance but also by a range of other non-energy providers and organisations. This calls for demand reduction policies to engage wider coalitions of service ‘stakeholders’. In addition, because energy-services co-constitute meta-services, aspirations to deliver the same levels of service but more efficiently risk entrenching, rather than reducing, levels of service demand. Implications for service-based business models (servicizing) and policies are discussed.

Ventilation in domestic buildings can have a significant impact on energy consumption but it is notoriously difficult to quantify, requiring physical test methods that are cumbersome and costly to apply. Energy demand reduction analyses routinely neglect the impact of ventilation and so offering well balanced advice for specific households is not possible. This paper describes a simple steady-state, heat-balance calculation method that is supplemented with monitoring data to model the effectiveness of reducing ventilation rates to minimum standards for individual homes. A key step is to determine the daily mean air change rate and the method is shown to yield plausible estimates that can then be used to establish the impact on energy demand. The benefits of the approach include improved energy demand disaggregation, customised energy demand reduction assessment and offers a means to underpin the development of better models of ventilation for building performance analysis.

This paper proposes an innovative algorithm for predicting short-term electrical maximum demand by using historical demand data. The ability to recognize in peak demand pattern for commercial or industrial customers would propose numerous direct and indirect benefits to the customers and utility providers in terms of demand reduction, cost control, and system stability. Prior works in electrical maximum demand forecasting have been mainly focused on seasonal effects, which is not a feasible approach for industrial manufacturing facilities in short-term load forecasting. The proposed algorithm, denoted as the Adaptive Rate of Change (ARC), determines the logarithmic rate-of-change in load profile prior to a peak by postulating the demand curve as a stochastic, mean-reverting process. The rationale behind this analysis, is that the energy efficient program requires not only demand estimation but also to warn the user of imminent maximum peak occurrence. This paper analyzes demand trend data and incorporates stochastic model and mean reverting half-life to develop an electrical maximum demand forecasting algorithm, which is statistically evaluated by cross-table and F-score for three different manufacturing facilities. The aggregate results show an overall accuracy of 0.91 and a F-score of 0.43, which indicates that the algorithm is effective predicting peak demand in predicting peak demand.

Extraordinary oil price declines were observed from 2008 to 2009 and from 2014 to 2016. During these periods, international oil prices collapsed by more than one half. Several reasons have been suggested for these crashes by numerous industry experts and scholars. In this study, we propose a new approach to explain the causes of the extreme oil price plummets via the supply and demand factors of price determinants. The autoregressive models with exogenous variables reflecting real demand, speculative demand, and supply factors are proposed and applied for forecasting monthly global crude oil prices during their periods of decline. Using the forecasting results, we retrospectively try to discover the factor that is best able to improve forecasting performance. During the first period of oil price decline, real demand reduction seemed to play a more prominent role compared with the other factors. Meanwhile, some supply factors, measured by U.S. shale oil production, combined with real and speculative demand factors, played an important role during the period of second oil price drop.

Energy consumption and greenhouse gas (GHG) emissions of China’s road transport sector have been increasing rapidly in recent years. Previous studies on the future trends trend to focus on the national picture and cannot offer regional insights. We build a novel bottom-up model to estimate the future energy demand and GHG emissions of China’s road transport at a provincial level, considering local economic development, population and policies. Detailed technical characteristics of the future vehicle fleets are analyzed in several up-to date scenarios. The results indicate that China’s vehicle stock will keep increasing to 543 million by 2050. The total direct petroleum demand and associated GHG emissions will peak at 508 million tonnes of oil equivalent (Mtoe) and 1500 million tonnes CO2 equivalent (Mt CO2,e) around 2030 in the Reference scenario. Natural gas vehicle diffusion has a large impact on petroleum demand reduction in the short term, with decreases of 41–46 Mtoe in 2050. Compared to the Reference case, battery electric and fuel cell vehicles will reduce petroleum demand by 94–157 and 28–54 Mtoe in 2050, respectively. When combined with decarbonization of future power supply, battery electric vehicles can play a significant role in reducing Well-to-Wheels GHG emissions in 2050 with 295–449 Mt CO2,e more reductions. The spatial distributions of future vehicle stock, energy demand and GHG emissions vary among provinces and show a generally downward trend from east to west. Policy recommendations are made in terms of the development of alternative fuels and vehicle technologies considering provincial differences, expansion of natural gas vehicle market and acceleration of electric vehicle market penetration.

In Vietnam, like many countries in East and Southeast Asia, the government has invested heavily in center-based compulsory treatment (CCT) as the mainstay demand reduction strategy for illicit drug use. This approach has been criticised on human rights grounds. Meanwhile, community-based voluntary methadone maintenance treatment (MMT) has been implemented for nearly a decade with promising results. To date, there have been no comparative Vietnamese studies of these approaches.The study, involving 208 CCT participants and 384 MMT participants with heroin dependence, was a combined retrospective and prospective observational study conducted over three years between 2012 and 2014 (with data at five time-points). The primary outcome was: self-report heroin use (confirmed by urinalysis). The four secondary outcomes were: illegal behaviours, overdose, blood-borne virus (BBV) risk behaviours, and monthly drug expenditure. Mixed effects regression analyses, which took into account baseline differences between the groups, were used to analyse the data. This study is registered with ClinicalTrials.gov, number NCT03071315.The study found MMT was more strongly associated with four outcome measures compared to CCT (reduction in heroin use (β = 3.39, SE = 0.31, p .0001) (equivalent to an odds ratio of 29.67 (95% CI 21.76-40.45)), reduction in illegal behaviours (β = 0.94, SE = 0.39, p .0001), (equivalent to an odds ratio of 2.56 (95% CI 1.79-3.78)), reduction in BBV risk behaviours (β = 1.08, SE = 0.17, p .0001), (equivalent to an odds ratio of 2.94 (95% CI 2.48-3.49)), and reduction in monthly drug spending (β = -VND1,515,200 (equivalent to US$72.00), SE = VND452,900, p .0001)). The analyses did not support the hypothesis that MMT was associated with better outcomes pertaining to overdose (β = -0.27, SE = 0.30, p = .62), probably due to the infrequency of these self-reported events.Our observational study suggests that MMT is associated with greater reductions in heroin use, BBV risk behaviours, drug-related illegal behaviours, and monthly drug spending compared with CCT. In the context that the CCT approach has been criticized for human rights violations, this study provides evidence to support the scale up of MMT and the transition of CCT to voluntary community based treatment.

Developing energy independent and sustainable communities has gained a high level of global interest in recent times. The Federal Sustainable Development Strategy for Canada (FSDS) has defined the addressing of climate change and the shrinking of the environmental footprint as priority themes in its sustainable development approach. In the pursuit of planning net-zero to net-positive community energy systems, reducing the energy demand of the residential buildings is the first priority, particularly as the residential sector accounts for 17% of the secondary energy use in Canada. Various technical, behavioural, and policy level interventions are available for reducing residential energy consumption. In this review, demand reduction interventions have been categorised as efficiency, saving and conservation measures. The benefits, limitations and challenges present in adopting those strategies are discussed for various regions in Canada. Even though there is an advanced level of knowledge available on energy demand reduction, the scattered information needs to be brought together to develop a combined and inclusive approach for residential demand management. Collected knowledge will inform decision makers engaged in the design of residential buildings and planning of sustainable communities to identify the interventions which can be implemented in a given locality. This review aims to address the above gaps through a comprehensive review on potential energy demand reduction methods, especially for Canada. The implications of the findings to Canadian residential energy sector as a whole are discussed in the final part of the review.

Building integrated photovoltaics is an envelope solution that combines energy generation, net energy demand reduction, and aesthetics. Lighter and semi-transparent technologies made the integration of photovoltaic cells in windows possible, which could not be achieved with traditional silicon technologies. This article aims to propose a new method to measure building energy performance using parametric models, simulation, and the use of genetic algorithms, resulting in a more precise and time-saving process compared to regular methods. Three different geometries and six facade configurations were designed, window-to-wall ratio and orientation were set as variables to optimize energy demand reduction. The software used is Rhinoceros, alongside the plugins Grasshopper, Ladybug, Honeybee, and Galapagos, running all simulations and optimizations in a single platform. Galapagos performs the optimizations, an automatic process that required on average 600 simulations to find the optimal solution, while regular methods take up to 9,720 possibilities. Although the benefits from building integrated photovoltaics vary depending on building geometry and envelope configuration, a net energy demand reduction was achieved in all cases. Such values ranged from 6.76%, reached by low-rise buildings with one photovoltaic facade, to 24.04%, accomplished by mid-rises where all facades had window-integrated photovoltaics.

The global trade in wildlife affects ~24% of terrestrial vertebrates, and demand for traditional medicinal materials, especially for traditional Chinese medicine, is a high profile driver. Much research has established a causal link between demand for medicinal materials for "TCM" and negative impacts on species conservation and on individual animals’ welfare. Key hopes for reducing these impacts are demand reduction and redirection strategies, targetted at consumers and professionals. Conservation research papers routinely treat "TCM" as a homogenous entity, and we argue that in so doing fail to identify distinct markets or communities within "TCM", and that recognising these distinctions would facilitate strategies for demand reduction and redirection. We present an initial taxonomy of "TCM" - using medicinal materials derived from wild animal species as a proof of concept - separating it into three principal components: (a) zhongyi is the broad, all-inclusive medical field representing diverse medicinal materials used in so-called pre-modern and modern medical practice, and described in a number of traditional and revived modern texts; (b) TCM represents a regulated suite of medical and pharmaceutical practises that began to be established from zhongyi in the 1950s and also belongs among zhongyi practices today. Medicinal materials within TCM which represent a curated subset of those within wider zhongyi, are described in the Pharmacopoeia of the People’s Republic of China, and are subject to change (for example if trade in a species becomes strictly regulated); finally, (c) CMP, ‘Chinese medicine and pharmaco-therapy’ is a neo-liberal extension to mainly TCM but also to some aspects of zhongyi. It represents a highly commodified and commercialised form of TCM and zhongyi and includes also some newly designed health products not previously considered ‘traditional medical’ - let alone ‘traditional Chinese medical’ - which are dispensed in drug shops, frequently in the absence of a medical practitioner. Practitioners, suppliers and potentially consumers in each category of what in conservation circles is labelled using the blanket term "TCM" are likely to regard themselves as distinct from the others. This appreciation raises the possibility of working with official TCM authorities, professional bodies, academics and practitioners to reduce, and perhaps eliminate, the use of species of conservation and animal welfare concern.

Our analysis of policy options was motivated by an inexplicable under-investment in demand response (DR) in the U.S. state of Georgia. In addition to estimating the size of the DR gap, we identify its causes and consequences. By modifying parameters of the U.S. flagship National Energy Modeling System (NEMS), we generate a baseline DR forecast with a default 4% maximum on-peak demand reduction, an achievable case with a DR limit of 20%, and a technical scenario that also halved the cost of storage. The results document many benefits of DR including a demand-reduction induced price effect (DRIPE), which makes DR more equitable than many other clean-energy policies that shift costs to non-participants. Our modeling results, literature review, and focus group analysis enable identification of DR barriers and motivators related to financial costs, electricity rates, consumer bills, pollution emissions, public health, energy equity, and inclusion. Our results suggest that the DR gap is caused less by technology limitations than by the need for financing initiatives, market innovations, infrastructure modernization, and enablers of socio-economic inclusion. By studying a state that lags in DR implementation, other countries and sub-national entities where DR is under-utilized can learn from our findings and methods.

Efficient trading under wholesale market competition reduces an electric grid’s energy costs for meeting time- and location-dependent demands. We analyse energy trading efficiency by estimating three newly developed sets of energy price difference regressions interconnected by their common root of energy price level regressions. Using a sample of ~ 0.6 million hourly observations for 01/01/2014 to 10/31/2020, our estimation documents energy trading efficiency of the 10 local resource zones across 15 American states administered by the Midcontinent Independent System Operator (MISO). We find MISO’s zonal energy markets integrated across space (zone j vs. zone k for j ≠ k) and time (day-ahead market vs. real-time market). However, these markets exhibit inter-day energy prices differences that move with the fundamental drivers (e.g., day-ahead forecasts for natural gas price and zonal wind generation) of day-ahead energy prices and their forecast errors. Inter-zonal day-ahead energy price differences move with the fundamental drivers and their zonal variations. Inter-zonal real-time energy price differences increase with inter-zonal day-ahead energy price differences and depend on zonal variations of the fundamental drivers’ forecast errors. Our empirics yield two important policy implications. First, enhancing inter-day trading efficiency requires accuracy improvements in (a) day-ahead forecasts for natural gas price, zonal load levels, and zonal wind generation, and (b) day-ahead scheduling of zonal nuclear and must-run generation. Second, improving inter-zonal trading efficiency requires mitigating inter-zonal transmission congestion via transmission capacity expansion, generation investment and demand reduction in a load pocket, and virtual bidding for inter-zonal energy price differences.

The coronavirus pandemic (COVID-19) has led to a massive collapse in economic activity and energy demand, with the result of significant emissions reductions at a global scale. However, the existing literature investigating abatement from COVID-19 mainly overlooked the overwhelming emissions reduction in Europe's power sector. We address this by assessing the intricate relationship between electricity demand shocks and heterogeneous generation technologies in the power sectors of 16 major European economies during January to March 2020. We apply an econometric model in an instrumental-variables framework. In a first step, we assess the impact of COVID-19 infections on electricity demand, and in a second step how this translates into emissions abatement. We find that, during full lockdown, COVID-19 reduced electricity demand by 19% and carbon emissions by an astonishing 34% per hour, whereas there is severe country heterogeneity depending on the electricity supply structure and demand shock intensity. From our estimates, we predict that power sector emissions fell by 18.4% in 2020. Our results reveal the importance of a carbon price, so that a demand reduction can offset large amounts of emissions by displacing coal at the margin. We derive several policy implications from our analysis to draw lessons from the pandemic.

With a drive towards achieving an integrated energy system, there is a need for holistic and scalable building modelling approaches for the commercial building stock. Existing grey-box modelling approaches often fail to produce a generalised network structure, which limits the suitability of models for different applications. Furthermore, existing feature assessment frameworks provide limited opportunities to quantify the potential of model characteristics in terms of flexibility, scalability and interoperability. Considering the diversity of the possible characterisation approaches, this study aims to define and assess a set of basic and derived features for reduced-order grey-box models through a generalisable framework that would act as a decision support tool for the identification of appropriate model characteristics. This research proposes an integrated methodology to test and evaluate model features, namely, scalability, flexibility, and interoperability for reduced-order grey-box models and formulates test-cases with the available commercial reference buildings published by the Department of Energy of the United States. The model scalability errors lie between 3.42% and 4.35% that indicates the suitability of implementing a zone level model for model predictions at the whole building level. The model flexibility error decreased from 5.73% to 4.78% when considering a trade-off between accuracy and complexity. These frameworks produce scalable and flexible models that facilitate urban energy modelling of building stocks and subsequent evaluation of retrofit strategies. Furthermore, the devised models aid the implementation of heat demand reduction scenarios in a building cluster to achieve an integrated energy system.

As new technology is introduced, demand for certain materials will increase and demand for others will diminish. Base metal supply is generally elastic and can be expected to respond to demand. However, most minor metals—or “companion metals”—are coproduced with base metals and therefore have severely limited supply elasticity. If technological or societal change leads to a major demand reduction for a base metal, this could cause significant supply constraints for its companion metals. “Black swan” events are unexpected disruptions with major consequences. In this work, we show that not all companion metals behave the same when faced with a black swan base metal supply disruption, in our case Cu and Zn. We model the large-scale adoption of graphene for both electricity transport and corrosion protection, resulting in 25% and 33% reductions in demand over the 2025–2040 period for Cu and Zn, respectively. We find that companion metals fall into three categories: 1) the nuisance companion, 2) the low-volume companion, and 3) the profitable companion. Cadmium is an example of a nuisance companion, and as of 2014, half of the available Cd was discarded in some form rather than refined into metal. A supply disruption would lead to a reduced Cd oversupply but not to constraints. Germanium and indium are examples of low-volume companions and show a modest supply deficit of 20–50% in 2050 if confronted with a black swan event. Both elements face challenges in scaling up production significantly beyond current coproduction levels with Zn. Silver and molybdenum are profitable companions, and the overall market of these metals would only be moderately impacted by a black swan event (10–20% deficit) because other sources exist. These results indicate policy makers concerned about materials criticality should focus their limited resources on low-volume companion metals.

The growing penetration of distributed energy generation (DEG) is causing major changes in the electricity market. One key concern is that existing tariffs incentivize ‘free riding’ behavior by households, leading to a cycle of rising electricity prices and DEG adoption, thereby eroding utility revenues and start a death spiral. We developed an agent based model using data from two cities in the U.S. to explore this issue. Our model shows worries about a utility ‘death spiral’ due to the adoption of rooftop solar, under current policies and prices in the U.S., are unfounded. We found, consistently for a number of scenarios, that, while the residential segment is impacted more heavily than the non-residential segment, the scale of PV penetration is minimal, in terms of overall demand reduction and subsequent tariff increases. Also, the rate of adoption would probably be smooth rather than sudden, giving the physical grid, the utility companies, and government policies enough time to adapt. Although our results suggest that fears of a utility death spiral from solar systems are premature, regulators should still monitor revenue losses and the distribution of losses from all forms of DEG. The concerns should lead to a more focus on tariff innovations.

We develop a new convex optimization (CO)-based charge/discharge scheduling algorithm for distributed energy storage systems (ESSs) co-located with solar photovoltaic (PV) systems. The CO-based scheduling algorithm minimizes the monthly electricity expenses of a customer who owns an ESS and incorporates both a time-of-use volumetric tariff and a demand charge tariff. Further, we propose the novel idea of a “supply charge” tariff that incentivizes ESS customers to store excess solar PV generation that may otherwise result in reverse power flow in the distribution grid. By means of a case study we observe the CO-based daily charge/discharge schedules reduce (1) peak net demand (that is, load minus PV generation) of the customer, (2) power fluctuations in the customer net demand profile, and (3) the reliance of the customer on the grid by way of promoting energy self-consumption of local solar PV generation. Two alternate methods for behind-the-meter ESS scheduling are considered as benchmarks for cost minimization, peak net demand reduction, and mitigation of net demand fluctuations. The algorithm is tested using real 30-min interval residential load and solar data of 53 customers over 2-years. Results show that the CO-based scheduling algorithm provides mean peak net demand reductions between 46% and 64%, reduces mean net demand fluctuations by 25–49%, and increases the mean solar PV self-consumption between 24% and 39% when compared to a customer without an ESS. Introduction of a supply charge reduces the maximum solar PV power supply to the grid by 19% on average and does not financially impact ESS owners.

This paper proposes a novel incentive-based demand response model from the view of a grid operator to enable system-level dispatch of demand response resources. The model spans three hierarchical levels of a grid operator, multiple service providers, and corresponding customers. The grid operator first posts an incentive to service providers, who will then invoke sub-programs with enrolled customers to negotiate quantities of demand reduction via providing service provider incentives. In view of this hierarchical decision-making structure, a two-loop Stackelberg game is proposed to capture interactions between different actors. The existence of a unique Stackelberg equilibrium that provides optimal system solutions is demonstrated. Simulation results show that the proposed approach is effective in helping compensate system resource deficiency at minimum cost.

This work presents a method to evaluate, from the distribution utility's perspective, the impacts of adopting Domestic Solar Water Heating (DSWH) systems in Brazil, based on demand measurements carried out on two groups of residential households. For the 12 months period evaluated, the case-study shows that using DSWH results in savings of 198 kWh (38%), and active power demand savings at peak times of 860 W (42%) per individual unit. For the distribution utility and per individual unit, using DSWH leads to avoided costs of $ 27.59 (38%), avoided costs due to peak time demand reduction of $ 170.2 (42%), and annual avoided costs of $ 197.79 (41%). This work clearly shows that in the residential section the tariff model and structure is strongly distorted, since it only accounts for the energy consumption per residential unit, and completely disregards the costs associated with power demand availability. Our results show that in Brazil the distribution utility charges only some 15% of the real cost of a hot shower based on electric showerheads.

Solar PV and battery storage technologies are known to provide savings to customers in the form of reduced electricity charges. Currently, these savings are only determined for the volume component (kWh) and not the demand component (kW or kVA). As interest grows in commercial solar PV and battery storage installations, the need to predict demand charge reductions is great. The aim of this research is to determine, with accuracy and reliability, the ability of solar PV and battery storage technologies in reducing demand charges. Results have shown that when simulated against a commercial-scale electricity consumption profile solar PV was able to reduce the maximum demand across five electricity networks in Australia by 0.05–1.51%. When coupled with a 12 kWh battery storage an additional 1.31–2.02% reduction was experienced. Battery utilisation strategy was shown to be critical in yielding greater demand reduction from the battery storage. Notably, it was shown that in the Ergon Energy electricity network, battery storage was able to supply demand at 34% lower cost ($/kW) than the network was able to. The results detail the first instance of demand reduction evaluation of solar PV coupled with battery storage, focusing on physical and financial outcomes in an Australian context.

More than a decade in the making, America's opioid crisis has morphed from being driven by prescription drugs to one fuelled by heroin and, increasingly, fentanyl. Drawing on historical lessons of the era of National Alcohol Prohibition highlights the unintended, but predictable impact of supply-side interventions on the dynamics of illicit drug markets. Under the Iron Law of Prohibition, efforts to interrupt and suppress the illicit drug supply produce economic and logistical pressures favouring ever-more compact substitutes. This iatrogenic progression towards increasingly potent illicit drugs can be curtailed only through evidence-based harm reduction and demand reduction policies that acknowledge the structural determinants of health.

Local water harvesting, demand reduction and wastewater treatment measures at a building scale can help manage water resources around the Mediterranean. This study conducts a life cycle water, energy and cost analysis of rainwater and condensate water harvesting, water conservation, wastewater treatment and three combinations of these measures over 50 years, using an apartment building in Sehaileh, Lebanon. The measures’ performance is further investigated through a comprehensive sensitivity analysis that extends the applicability of findings. Reducing water demand in the building through efficient water fixtures and appliances can decrease annual water and primary energy use by 1 534 kL and 130 GJ, respectively, while saving 530 USD every year. Wastewater treatment requires 230 MJ and costs 0.97 USD per kL of water saved, necessitating subsidies to be competitive. Rainwater and condensate water harvesting cover 4.8% and 1% of the total demand, respectively, but can reduce infrastructure spending, avoid water leakage and reduce urban run-off. Combining all measures reduces mains water supply by up to 75% for 1 MJ/kL saved and zero life cycle cost. Finally, a metered, seasonal and demand-driven water pricing scheme is recommended to better relate building occupants with the intermittent and scarce nature of water around the Mediterranean.

This article shines a light on a less examined aspect of sustainable energy transitions: governing for demand side innovations in Germany. Demand innovations are considered to be central to affordable, efficient and politically acceptable energy system transformations, however many argue that not enough is being done in governance terms. In a departure from much analysis on demand policy demand innovations are defined broadly here to explicitly include demand side response, demand reduction and distributed energy – given that each has important roles to play within demand-oriented markets. Demand governance is conceptualised as a long-term political process that is both contextually specific but also open to challenge and change at various points in time. The single case study is Germany where demand governance, recent changes in energy markets, and implications for how the politics of energy are changing are all analysed. This paper reveals the specific ways in which critical policy debates emerge over time and influence political decision-making; the ways in which these debates relate to changes in energy markets; as well as a lack of governance in relation to enabling demand side response and local energy markets.

This paper presents a parametric study on the effect of four green-roof types on outdoor/indoor temperature and cooling demand under four different climates and three urban densities using co-simulation approach with ENVI-met and EnergyPlus. Results reveal an outdoor nighttime warming effect of not more than 0.2 °C which is most obvious with the semi-extensive while the outdoor and indoor cooling effect ranges between 0.05–0.6 °C and 0.4–1.4 °C, respectively depending on the green-roof type, urban density and time of the day. These daytime temperature reductions also vary per prevailing climates and follow this order: hot-dry, hot (or warm)-humid, and temperate which can be explained by the interplay between solar intensity/air temperature and relative humidity between the regions. In a hot-humid region, the evaporative cooling potential of greenery is dampened when compared to hot-dry region. This is also true for region with low solar intensity and humidity like the temperate region. In terms of cooling demand reduction, 5.2% was observed in hot-dry climate on the hottest day of the year with full-intensive green-roof while the least saving of 0.1% was found with semi-extensive green-roof in temperate climate. In general, for both outdoor temperature and cooling demand reduction, semi-intensive green-roof was found more effective than its full-extensive counterpart while the higher spatial green-roof is most important for indoor temperature reduction irrespective of the leaf density of the greenery. Therefore, the intent of green-roof installation should be a determining factor for the type and spatial extent to be implemented.

The Netherlands have been a pivotal supplier in Western European natural gas markets in the last decades. Recent analyses show that the Netherlands would play an important role in replacing Russian supplies in Germany and France in case of a Russian export disruption. Lately, however, the Netherlands have suffered from a series of earthquakes that are related to the natural gas production in the major Groningen field. By consequence, natural gas production rates – that are politically mandated in the Netherlands – have been substantially reduced, by almost 45% in 2015 compared to 2013-levels. We implement this reduced production path for the next decades in the Global Gas Model and analyse the geopolitical impacts. We find that the diversification of European natural gas imports allows spreading the replacement of Dutch natural gas over many alternative sources, with diverse pipeline and LNG supplies. There will be hardly any price or demand reduction effect. Even if Russia fails to supply Europe, the additional impact of the lower Dutch production is moderate. Hence, the European consumers need not to worry about the declining Dutch natural gas production and their security of supplies.

Heavy Goods Vehicles (HGVs) contribute a large proportion (about 40%) of the emissions of air pollutants while only representing a small proportion (about 10%) of all transport operations. In Ireland, the Dublin Port Tunnel (DPT) was opened in 2006 as a dedicated route for HGVs between Dublin Port and the motorway system in order to reduce the HGV volume in the city centre. An HGV management strategy to restrict HGVs travelling through the city centre was also introduced. The aim of this study was to estimate the emission changes brought about by these infrastructural and regulatory changes. A transport model built in VISUM was utilized. Emissions were calculated using COPERT 4. The results showed that the DPT and HGV management strategy reduced the traffic in the city centre, and the HGV management improved traffic speed distribution. However the DPT and HGV management resulted in vehicles travelling further (travel distance increased by 16% and 51%, respectively) and increased the total emissions (increased by 8% and 21% in NOx, respectively). Total traffic and emission changes over time in Dublin were also estimated in this study. The traffic conditions and emissions in 2006, 2007 and 2013 were evaluated and the results indicated that a travel demand reduction in 2013 could also improve speed distribution. Emissions reduced from 2006 to 2013 and the fleet technology improvements had a positive impact on this reduction. The study shows that a traffic management policy and/or infrastructure change may bring about some localised environmental benefits within the management area; however, such a policy does not always reduce the total traffic emissions in the network as a whole and the impact to the wider environment could be negative in some circumstances.

Summary Background The WHO Framework Convention on Tobacco Control (WHO FCTC) has mobilised efforts among 180 parties to combat the global tobacco epidemic. This study examined the association between highest-level implementation of key tobacco control demand-reduction measures of the WHO FCTC and smoking prevalence over the treaty's first decade. Methods We used WHO data from 126 countries to examine the association between the number of highest-level implementations of key demand-reduction measures (WHO FCTC articles 6, 8, 11, 13, and 14) between 2007 and 2014 and smoking prevalence estimates between 2005 and 2015. McNemar tests were done to test differences in the proportion of countries that had implemented each of the measures at the highest level between 2007 and 2014. Four linear regression models were computed to examine the association between the predictor variable (the change between 2007 and 2014 in the number of key measures implemented at the highest level), and the outcome variable (the percentage point change in tobacco smoking prevalence between 2005 and 2015). Findings Between 2007 and 2014, there was a significant global increase in highest-level implementation of all key demand-reduction measures. The mean smoking prevalence for all 126 countries was 24·73% (SD 10·32) in 2005 and 22·18% (SD 8·87) in 2015, an average decrease in prevalence of 2·55 percentage points (SD 5·08; relative reduction 10·31%). Unadjusted linear regression showed that increases in highest-level implementations of key measures between 2007 and 2014 were significantly associated with a decrease in smoking prevalence between 2005 and 2015). Each additional measure implemented at the highest level was associated with an average decrease in smoking prevalence of 1·57 percentage points (95% CI −2·51 to −0·63, p=0·001) and an average relative decrease of 7·09% (−12·55 to −1·63, p=0·011). Controlling for geographical subregion, income level, and WHO FCTC party status, the per-measure decrease in prevalence was 0·94 percentage points (−1·76 to −0·13, p=0·023) and an average relative decrease of 3·18% (−6·75 to 0·38, p=0·079). This association was consistent across all three control variables. Interpretation Implementation of key WHO FCTC demand-reduction measures is significantly associated with lower smoking prevalence, with anticipated future reductions in tobacco-related morbidity and mortality. These findings validate the call for strong implementation of the WHO FCTC in the WHO's Global Action Plan for the Prevention and Control of Non-communicable Diseases 2013–2020, and in advancing the UN's Sustainable Development Goal 3, setting a global target of reducing tobacco use and premature mortality from non-communicable diseases by a third by 2030. Funding Health Canada, Canadian Institutes of Health Research, Ontario Institute for Cancer Research and Canadian Cancer Society Research Institute.

The present study focuses on the potential applicability of attached sunspaces in Southern and Central Europe (winter season). Sunspace is a diffuse passive solution which reduces winter thermal losses and increases solar gains. This article analyses the geo-climatic applicability of this technology following a two-step study: 1) a climate based pre-evaluation of sunspace potential for 50 selected locations representing different climatic situations; 2) a calculation of sunspace potential in reducing energy consumption for heating using energy dynamic simulations on a sample building by comparing different technological choices. Step 1 examines heating demand (Heating Degree Days - HDD) and total solar radiation on a horizontal plane for each location. HDD was assumed as an indicator of the heating demand at different base temperatures (20 ± 2 °C), while total winter solar radiation is considered as a general indicator of the “virtual” heating potential of attached sunspaces. Both calculations are based on a typical meteorological year (TMY). Step 2 is based on energy dynamic simulations of a sample building in order to analyse heating energy reduction caused by an attached sunspace on a monthly basis. Different technological choices were taken into account for both sunspaces and building envelopes in order to identify the best local solutions for passive heating as regards energy consumption in a set of 50 selected locations. Considerations and potential maps are reported to help designers optimise the applicability of sunspaces according to local climatic conditions and building technologies. Finally, the summer performances and the potential cooling demand reduction by shading and natural ventilation of sunspaces are analysed.

peer-reviewed Food waste in hospitals is of major concern for two reasons: first, healthcare needs to move toward preventative and demand led models for sustainability and second, food system sustainability needs to seek preventative measures such as diet adaptation and waste prevention. The impact of breast-milk substitute use on health services is well established in literature in terms of healthcare implications, cost and resourcing, however as a food demand and waste management issue little has been published to date. This paper presents the use of a desk based backcasting method to analyse food waste prevention, mitigation and management options within the Irish Maternity Service. Best practice in healthcare provision and waste management regulations are used to frame solutions. Strategic problem orientation revealed that 61% of the volume of ready to use breast-milk substitutes purchased by maternity services remains unconsumed and ends up as waste. Thirteen viable strategies to prevent and manage this waste were identified. Significant opportunities exist to prevent waste and also decrease food demand leading to both positive health and environmental outcomes. Backcasting methods display great promise in delivering food waste management strategies in healthcare settings, especially where evidenced best practice policies exist to inform solution forming processes. In terms of food waste prevention and management, difficulties arise in distinguishing between demand reduction, waste prevention and waste reduction measures under the current Waste Management Hierarchy definitions. Ultimately demand reduction at source requires prioritisation, a strategy which is complimentary to health policy on infant feeding.

There is an opportunity to reduce the amount of sheet metal currently used to manufacture automotive components, despite the available cost and CO2 savings, the automotive industry has not realised the full potential of these saving opportunities. To understand why, a practical case study was set up with an automotive manufacturer. A cross-functional team was established with the scope to make changes to five components using a structured design process to improve material efficiency. The trial identified realistic opportunities to improve material utilisation by 20%pts, and save £9million and 5 kilotonnes of CO2 annually. The greatest saving opportunities were found early in the product development cycle, before the production method is determined by component geometry. Of these, 3%pts were actually implemented on the production vehicle, saving £1.8million and 1.5 kilotonnes of CO2 annually. The case study identified significant barriers to implementing material efficiency strategies in an industrial setting. To overcome these barriers material utilisation should be considered early in the product design process and high in the vehicle platform hierarchy. As a result of this investigation, new business processes are being generated to support design for material utilisation at the automotive manufacturer. This case study approach should be considered to increase implementation for other aspects of material demand reduction.

Space heating accounts for the most significant share of final energy demand in buildings in colder climatic regions contributing to greenhouse gas emission. In addition, there is a lack of data available to assess spatially, the potential of space heating demand reduction in different buildings when considering typical building refurbishment measures. Hence, in this paper, a robust information socket for urban building stock is developed to assess the impact of energy efficiency measures on space heating demand savings and CO2 emission reduction potential in the existing buildings based on a Geographical Information System tool. The model considers the topology and thermal performance of different building categories; houses with and without thermal insulation, apartments, commercial, public, office, and industrial buildings. Three scenarios, a reference and two additional scenarios using Prishtina city as a case study, have been created for standard (scenario 1), and advanced energy efficiency (scenario 2) measures based on the government’s proposed policies. The findings show that space heat demand saving potential for scenario 1 and 2 in comparison to the reference scenario was 50% and 68.5% respectively. Moreover, the CO2 emissions are reduced significantly from 502.3 mil kgCO2/year in reference scenario to 249.8 mil kgCO2/year for scenario 1 and 158.7 mil kgCO2/year in scenario 2 respectively.

This paper presents an evaluation framework for Techno-Economic-Environmental (TEE) impact of different networks integration levels and storage devices on performance of Integrated Gas and Electricity Networks (IGENs). Probabilistic distributions for modelling sources of uncertainty (loads, Renewable Energy Sources (RESs), economic and environmental analysis) were sampled through Monte Carlo Simulation. The framework performs the TEE operational analysis of IGENs for future possible scenarios for different technology development status and different levels of load and RESs. Then, it calculates the energy imported from upstream networks, operational costs, and emissions. In this way, the framework provides a basis for making well-informed and risk-based decisions of design choices for meeting 2050 carbon targets in the presence of aforementioned sources of uncertainty. Analysis of the results of application of the framework to a real-world case study shows that as the electrical renewable generation grows with respect to the total demand, the value of integrated operation of the networks also grows as shown by the reduction in the TEE parameters. Given that demand reduction and decarbonisation of electricity and gas networks is a priority, the coupled configurations are likely to become more attractive between now and 2050, in the presence of the considered sources of uncertainty.

This paper presents a combined economic dispatch and demand response optimisation model under renewable obligation. Real data from a large-scale demand response programme are used in the proposed model. The model uses renewable obligation policy and direct load control to find an optimal energy and reserve strategy that minimises generation costs and maximising renewable penetration. The real data from the South African large-scale demand response programme are used in which the system operator can directly control the participation of electric water heaters at the substation level. The actual load before and after demand reduction are used to assist the system operator in making optimal decisions on whether a substation should participate on the demand response programme. The application of real data to the proposed model avoids the traditional approaches which assume arbitrary controllability of flexible demand. The effectiveness of the model is tested using the modified IEEE 30 and 118-bus systems. The results show that the proposed model can achieve significant demand reduction as high as 830 MW for the IEEE 30-bus system and shift up to 401 MW. Moreover, a total cost reduction of 21.56% can be achieved using the IEEE 118-bus system.

Energy load shifting can allow for increased renewable energy integration and reduced greenhouse gas intensity of the electricity grid. Recent research has demonstrated that wastewater treatment plants have considerable potential to shift energy loads and act as energy demand resources due to their energetic flexibility and energy production capacity. This paper investigates a wastewater treatment plant in Santa Rosa, California, participating as a demand resource on the wholesale energy market through the proxy demand resource program. Test demand response events showed that the facility was able to shift its energy load by modifying select operations without impacting wastewater effluent quality. A cost-benefit analysis based on projected program participation and the results from the test events, estimates that the Santa Rosa wastewater treatment plant could achieve up to 4.8% energy cost savings through the proxy demand resource program. Two main issues were identified from the test events: (1) the difficulty of correctly timing demand reduction periods and (2) the inaccuracy of using standard baseline methods to measure the energy load reduction. As a supplement to the case study, this paper also presents a roadmap outlining the technology necessary for wastewater treatment plants to participate in demand resource programs through energy load shifting. The roadmap identifies key instrumentation and automation infrastructure, and assets that can be utilized to provide energetic flexibility; it also recommends additional infrastructure that can stabilize energy loads and enhance controlled energy load shifting.