For better or for worse? Empirical evidence of moral licensing in a behavioral energy conservation campaign

Isolated environmental campaigns focusing on defined target behaviors are rolled out to millions of households every year. Yet it is still unclear whether these programs trigger cross-domain adoption of additional environment-friendly behaviors (positive spillover) or reduced engagement elsewhere. A thorough evaluation of the real net performance of these programs is lacking. This paper investigates whether positive or perverse side effects dominate by exemplifying the impact of a water conservation campaign on electricity consumption. The study draws on daily water (10,780 data points) and weekly electricity (1386 data points) consumption data of 154 apartments in a controlled field experiment at a multifamily residence. The results show that residents who received weekly feedback on their water consumption lowered their water use (6.0% on average), but at the same time increased their electricity consumption by 5.6% compared with control subjects. Income effects can be excluded. While follow-up research is needed on the precise mechanism of the psychological process at work, the findings are consistent with the concept of moral licensing, which can more than offset the benefits of focused energy efficiency campaigns, at least in the short-term. We advocate the adoption of a more comprehensive view in environmental program design/evaluation in order to quantify and mitigate these unintended effects

Overcoming Salience Bias : How Real-Time Feedback Fosters Resource Conservation

Inattention and imperfect information bias behavior toward the salient and immediately visible. This distortion creates costs for individuals, the organizations in which they work, and society at large. We show that an effective way to overcome this bias is by making the implications of one’s behavior salient in real time, while individuals can directly adapt. In a large-scale field experiment, we gave participants real-time feedback on the resource consumption of a daily, energy-intensive activity (showering). We find that real-time feedback reduced resource consumption for the target behavior by 22%. At the household level, this led to much larger conservation gains in absolute terms than conventional policy interventions that provide aggregate feedback on resource use. High baseline users displayed a larger conservation effect, in line with the notion that real-time feedback helps eliminate “slack” in resource use. The approach is cost effective, is technically applicable to the vast majority of households, and generated savings of 1.2 kWh per day and household, which exceeds the average energy use for lighting. The intervention also shows how digitalization in our everyday lives makes information available that can help individuals overcome salience bias and act more in line with their preferences

Anti-Counterfeiting and Supply Chain Security

Counterfeit trade developed into a severe problem for many industries. While established security features such as holograms, micro printings or chemical markers do not seem to efficiently avert trade in illicit imitation products, RFID technology, with its potential to automate product authentications, may become a powerful tool to enhance brand and product protection. The following contribution contains an overview on the implication of product counterfeiting on affected companies, provides a starting point for a structured requirements definition for RFID-based anti-counterfeiting systems, and outlines several principal solution approaches that are discussed in greater detail in the subsequent chapters.Thorsten Staake, Florian Michahelles, Elgar Fleisch, John R. Williams, Hao Min, Peter H. Cole, Sang-Gug Lee, Duncan McFarlane and Jun Mura

Counterfeiting Models (Mathematical/Economic)

Counterfeiting and piracy are illicit activities infringing IPR (intellectual property rights). The market for counterfeit can be divided into two important submarkets. In the primary market, consumers purchase counterfeit products believing they have purchased genuine articles (deceptive counterfeiting). In the secondary market, consumers knowingly buy counterfeit products (nondeceptive counterfeiting). Counterfeiting has, obviously, consequences on genuine producers and consumers; nevertheless, it can have general socioeconomic effects. There is a considerable body of theoretical and empirical literature on the mechanisms of counterfeit trade and on the economic and social effects of counterfeiting. A number of the methodological papers are undertaken within the framework of operations research and game theory

Economic assessment of photovoltaic battery systems based on household load profiles

Technical advances and decreasing costs of photovoltaic (PV) and battery (B) systems are key drivers for the consumer-prosumer transition in many countries. However, the installation of a photovoltaic-battery (PVB) system is not equally profitable for all consumers. This study systematically assesses how heterogeneity in real-world electricity load profiles affects the optimal system configuration and profitability of PVB systems. To that end, we develop a techno-economic simulation model that optimizes the PVB configuration for given electricity load profiles. The analysis uses real-world energy consumption data from 4190 households and is conducted for current electricity rates and weather conditions in Zurich, Switzerland. To account for future price reductions of PV and PVB systems, we conduct a sensitivity analysis that assesses how different cost scenarios influence optimal system configuration and profitability. Finally, we develop and validate a machine learning algorithm that can predict system profitability based only on a limited set of features and on shorter measurement timeframes of smart-meter data. We find that under the current cost scenario (PV: 2000 €/kWp, B: 1000 €/kWh) and without subsidies, about 40% of the analyzed households reach a positive net present value (NPV) for a PV-system, but only for 0.1% of households is the integration of a battery profitable. Under the most optimistic cost scenario for both technologies (PV: 1000 €/kWp, B: 250 €/kWh), 99.9% of the households benefit from the integration of battery storage into their optimal system configuration, with a mean installed PV power of 4.4 kWp and a mean battery size of 9.6 kWh. In all cost scenarios, system profitability varies considerably between households, even for households with comparable total annual demand, primarily due to the heterogeneity in the load profiles. Thus, being able to identify households for whom the installation is profitable is important. The proposed machine learning algorithm predicts optimal configuration, profitability, self-sufficiency, and self-sufficiency ratios with good accuracy, even when only relatively short timeframes of smart-meter data are available. The results of this study are relevant for households making individual investment decisions as well as for utility companies to more effectively identify and approach relevant customers for the installation of PVB systems. Furthermore, the findings enable policymakers to determine the critical levers for increasing private investments into PVB systems in their region and to predict how future developments like component costs will affect the future diffusion of these systems.ISSN:0306-2619ISSN:1872-911