Introduction to the special issue ‘Integrated scenario building in energy transition research

Adaptive governance approaches emphasize the crucial role of the private sector in enabling climate change adaptation. Yet, the participation of local firms is still lacking, and little is known about the conditions potentially influencing firms’ adaptation decisions and mechanisms that might encourage private sector engagement. We address this gap with an empirical analysis of the willingness of manufacturing small- and medium-sized enterprises (SMEs) to participate financially in collective flood adaptation in Ho Chi Minh City (HCMC), a hotspot of future climate change risk. Using scenario-based field experiments, we shed light on internal and external conditions that influence potential investments in collective initiatives and explain what role SMEs can play in flood adaptation. We find that direct impacts of floods, perceived self-responsibility, and strong local ties motivate firms to participate in collective adaptation, whereas government support, sufficient financial resources, and previously implemented flood protection strategies reduce the necessity to act collectively. Here, opportunity costs and the handling of other business risks play a decisive role in investment decisions. This study shows that although private sector engagement appears to be a promising approach, it is not a panacea. Collective initiatives on flood adaptation need formal guidance and should involve local business networks and partnerships to give voice to the needs and capacities of SMEs, but such initiatives should not overstretch firms’ responsibilities

Uncertainty analysis using Bayesian Model Averaging: a case study of input variables to energy models and inference to associated uncertainties of energy scenarios

Background Energy models are used to illustrate, calculate and evaluate energy futures under given assumptions. The results of energy models are energy scenarios representing uncertain energy futures. Methods The discussed approach for uncertainty quantification and evaluation is based on Bayesian Model Averaging for input variables to quantitative energy models. If the premise is accepted that the energy model results cannot be less uncertain than the input to energy models, the proposed approach provides a lower bound of associated uncertainty. The evaluation of model-based energy scenario uncertainty in terms of input variable uncertainty departing from a probabilistic assessment is discussed. Results The result is an explicit uncertainty quantification for input variables of energy models based on well-established measure and probability theory. The quantification of uncertainty helps assessing the predictive potential of energy scenarios used and allows an evaluation of possible consequences as promoted by energy scenarios in a highly uncertain economic, environmental, political and social target system. Conclusions If societal decisions are vested in computed model results, it is meaningful to accompany these with an uncertainty assessment. Bayesian Model Averaging (BMA) for input variables of energy models could add to the currently limited tools for uncertainty assessment of model-based energy scenarios

Applying Bayesian model averaging for uncertainty estimation of input data in energy modelling

Background Energy scenarios that are used for policy advice have ecological and social impact on society. Policy measures that are based on modelling exercises may lead to far reaching financial and ecological consequences. The purpose of this study is to raise awareness that energy modelling results are accompanied with uncertainties that should be addressed explicitly. Methods With view to existing approaches of uncertainty assessment in energy economics and climate science, relevant requirements for an uncertainty assessment are defined. An uncertainty assessment should be explicit, independent of the assessor’s expertise, applicable to different models, including subjective quantitative and statistical quantitative aspects, intuitively understandable and be reproducible. Bayesian model averaging for input variables of energy models is discussed as method that satisfies these requirements. A definition of uncertainty based on posterior model probabilities of input variables to energy models is presented. Results The main findings are that (1) expert elicitation as predominant assessment method does not satisfy all requirements, (2) Bayesian model averaging for input variable modelling meets the requirements and allows evaluating a vast amount of potentially relevant influences on input variables and (3) posterior model probabilities of input variable models can be translated in uncertainty associated with the input variable. Conclusions An uncertainty assessment of energy scenarios is relevant if policy measures are (partially) based on modelling exercises. Potential implications of these findings include that energy scenarios could be associated with uncertainty that is presently neither assessed explicitly nor communicated adequately