Environmental impacts and net-energy of hydrogen production via solar-electrolysis are highly sensitive to operating constraints and context specific variances.

The following article conducts an analysis of the Intergovernmental Panel on Climate Change (IPCC) Fifth Assessment Report (AR5), specifically in relation to Integrated Assessment Models (IAMs). We focus on the key drivers of economic growth, how these are derived and whether IAMs properly reflect the underlying biophysical systems. Since baseline IAM scenarios project a three-to eight-fold increase in gross domestic product (GDP)-per-capita by 2100, but with consumption losses of only between 3-11%, strong mitigation seems compatible with economic growth. However, since long-term productivity and economic growth are uncertain, they are included as exogenous parameters in IAM scenarios. The biophysical economics perspective is that GDP and productivity growth are in fact emergent parameters from the economic-biophysical system. If future energy systems were to possess worse biophysical performance characteristics, we would expect lower productivity and economic growth, and therefore, the price of reaching emission targets may be significantly costlier than projected. Here, we show that IAMs insufficiently describe the energy-economy nexus and propose that those key parameters are integrated as feedbacks with the use of environmentally-extended input-output analysis (EEIOA). Further work is required to build a framework that can supplement and support IAM analysis to improve biophysical rigour.

Solar photovoltaics (PV) is widely regarded as one of the most promising renewable energy technologies. Net energy analysis (NEA) is a tool to evaluate the energetic performance of all energy supply technologies, including solar PV. Results across studies can appear to diverge sharply, which leads to contestation of NEA's relevance to energy transition feasibility assessment and contributes to ongoing uncertainty in relation to the critical issue of the sustainability of PV. This study explores how PV NEA approaches differ, including in relation to goal definitions, methodologies and boundaries of analysis. It focuses on two principal NEA metrics, energy return on investment (EROI) and energy payback time (EPBT). Here we show that most of the apparent divergence between studies is accounted for by six factors-life-cycle assessment methodology, age of the primary data, PV cell technology, the treatment of intermittency, equivalence of investment and output energy forms, and assumptions about real-world performance. The apparent divergence in findings between studies can often be traced back to different goal definitions. This study reviews the differing approaches and makes the case that NEA is important for assessing the role of PV in future energy systems, but that findings in the form of EROI or EPBT must be considered with specific reference to the details of the particular study context, and the research questions that it seeks to address. NEA findings in a particular context cannot definitively support general statements about EROI or EPBT of PV electricity in all contexts.

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