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To the best of our knowledge, the previous Data Envelopment Analysis studies on passenger-car sustainability have neglected to measure technology-based measures, technology inequality, and technology diffusion barriers. To fill this research gap, this study proposes a new framework consisting of Data Envelopment Analysis, K-means clustering method, Gini coefficient, and the group-based decomposition method. Adopting these methods, this study examines the Marginal Rate of Transformation and the Rate of Substitution, identifies both desirable congestion (or eco-innovation) and undesirable congestion, evaluates technology inequality, and explores the main barriers to technology diffusion. Empirically, we assess new passenger cars released in the United States in 2020. The main conclusions are summarized as follows: First, under managerial disposability, the generally negative Marginal Rate of Transformation and Rate of Substitution across passenger cars suggest that the majority of car manufacturers are environmentally conscious. This outcome is partly the result of the minimum environmental standards set for car manufacturers. Second, the majority of passenger cars displayed the possibility of strong desirable congestion (clean-technology innovation) and no undesirable congestion simultaneously. The majority of passenger cars need to decrease carbon dioxide emissions along with an increase in fuel consumption efficiency (miles per gallon). This finding indicates that regulatory strategies have been mostly effective at directing eco-technological innovation toward mitigating a significant source of climate change. Finally, technology inequality was driven by both cross-group and within-group inequalities. Moreover, considerable heterogeneity existed in the within-group decomposition of the overall efficiency Gini coefficient result. • We measure marginal quantities and technology inequality of passenger cars. • We find that most passenger carlines are environmentally conscious. • An increase in desirable output could increase undesirable output. • Most passenger cars show possibilities of clean technology innovation. • Cross-group and within-group inequalities drive technology inequality. [ABSTRACT FROM AUTHOR]

Presents a study that evaluated impacts of educational diagnostic teams of consultants used to transfer technology to dairy farms. Changes observed in project herds; Information on the dairy industry in Minnesota; Results of the evaluation of the Dairy Diagnostic Team and demonstration farm concept.

Many energy systems models have sought to develop pathways for deep decarbonization of the global energy system. Most often, these pathways minimize system costs or greenhouse gas emissions; with few exceptions, they ignore the constraints imposed by political, social, and economic factors that slow transition processes, making them prone to producing implausible decarbonization pathways. This paper integrates a key socio-technical factor—social acceptance of low-carbon nuclear power—into an energy systems model to illustrate how it alters the optimal energy generation mix. The United States was chosen as the example, but the approach itself is designed to be general and applicable to any region of interest. An empirically grounded risk tolerance model is developed to characterize acceptance of nuclear power and estimate an upper-bound deployment limit for the technology. Illustrative scenarios are presented to improve our understanding of how the socio-technical constraints that exist in the real world can alter deep decarbonization pathways. The cost-optimal generation portfolio to achieve net zero CO 2 emissions by 2050 primarily relies on nuclear power. If risk tolerance concerns constrain nuclear deployment to socially acceptable levels, deep decarbonization scenarios are up to 11% more expensive than the reference scenario and require low-carbon options to be available and replace the reduced nuclear share. Results from this novel framework improve our representation of the effect of social acceptance on the adoption and diffusion of energy technologies. They also contribute to a growing literature that seeks to firmly embed the social sciences in climate and energy policy. • Plausible decarbonization pathways must also account for social constraints. • We combine a social risk tolerance model with an energy system optimization model. • We assess the role of social acceptance in constraining nuclear power's deployment. • Social acceptance limits nuclear's share by as much as 71% in CO 2 reduction pathways. • Decision-making can be strengthened with integration of socio-technical analyses. [ABSTRACT FROM AUTHOR]

The role of venture capital as mediator and gatekeeper is well acknowledged and geographical barriers for open innovation have been questioned, but venture capital firms' distant investments have been investigated only rarely. The strategic benefits accrued from corporate venture capital (CVC) investment depend on the selection of target ventures. Prior research, however, overlooked the incurred information cost for identifying a potential target. Considering that innovative ventures often reside in distant locations, this paper aims to investigate what factors alleviate the information cost for CVCs when identifying target ventures in distant locations. We expect a CVC's target selection in distant locations will be limited to the ventures under a tight appropriability regime, ventures within the same industries as a CVC's business units, and ventures with pre-existing investors that a CVC has prior ties with. The hypotheses are tested with the data on CVC investments in the U.S. between 2006 and 2013. The results empirically support the hypotheses. [ABSTRACT FROM AUTHOR]