Background: Treatment providers have applied contingency management (CM) treatment, an intervention that often rewards individuals for drug abstinence (i.e., ABS CM), to treatment engagement as well. However, we know little about the magnitude of treatment effects when providers target attendance behaviors (i.e., ATT CM). Methods: This study conducted a systematic search to identify studies that included ATT CM, either in isolation or in combination with ABS CM. The study used meta-analysis to estimate the effect size of ATT CM and ABS CM + ATT CM on treatment attendance and drug abstinence. We identified a total of 10 studies including 12 CM treatments (6 ATT CM and 6 ABS CM + ATT CM) with 1841 participants. Results: Results indicated a moderate effect (d = 0.47, 95% confidence interval (CI) [0.25, 0.69]) of ATT CM on attendance relative to non–reward active comparison conditions. Frequency of rewards was significantly associated with larger effect sizes. Results also indicated a small effect (d = 0.22, 95% CI [0.12, 0.33]) of ATT CM on abstinence outcomes relative to nonreward comparisons, p < 0.001. The study found no significant differences in attendance or abstinence between ATT CM and ABS CM + ATT CM (p's > 0.05). Conclusion: Overall, the results supported ATT CM for increasing treatment engagement, with smaller effects on abstinence. Effects on abstinence were smaller than those observed in prior meta-analyses focused on ABS CM. No significant differences existed in attendance or abstinence outcomes between ATT CM and ABS + ATT CM. However, future studies are needed to experimentally compare ABS CM + ATT CM to ABS CM, and ATT CM to determine additive effects. Clinics implementing CM should consider the differential effects between ATT CM and ABS CM when selecting target behavior(s).

Given the increasing demand for energy, climate change, and environmental concern of fossil fuels, it is becoming increasingly significant to find alternative renewable energy sources. Bioethanol as one sort of cellulosic biofuel produced from lignocellulosic biomass feedstocks has shown great potential as a renewable resource. Delivering a competitive, sustainable biofuel product requires comprehensive supply chain planning and design. Developing economically and environmentally optimal supply chain models is necessary in this context. Also, designing biomass bioethanol supply chain (BBSC) models addressing social issues requires using second-generation biomass which is not a source of food for humans. Currently, corn as a first-generation feedstock is the primary source of bioethanol in the United States which has given growth to new social issues such as the food versus fuel debate. Considering incentives for first-generation bioethanol producers to switch to second-generation biomass and associated production technologies will help to address such social issues. The scope of this study focuses on analyzing economic and environmental market incentives for second-generation bioethanol producers while considering different carbon policies as penalties and restrictions for emissions coming from BBSC activities. First, we develop an integrated life cycle emission and energy optimization model for analyzing an entire second-generation bioethanol supply chain using switchgrass as the source of biomass while finding the most appropriate potential locations for building new cellulosic biorefineries in North Dakota. Second, we propose a supply chain model by comparing a first-generation (corn) and a second-generation (corn stover) bioethanol supply chain to analyze how policymakers can incentivize first-generation bioethanol producers to switch their technology and biomass supply from first-generation to second-generation biomass. Third, we develop the model further by investigating the impact of four different carbon policies including the carbon tax, carbon cap, carbon cap-and-trade, and carbon offset on the supply chain strategic and operational decisions. This research will help to design robust BBSCs focused on sustainability in order to optimally utilize second-generation biomass resources in the future. The findings can be utilized by renewable energy policy decision makers, bioethanol producers, and investors to operate in a competitive market while protecting the environment.