Development of energy policy is often informed by economic considerations via aggregate production functions (APFs). We identify a theory-to-policy process involving APFs comprised of six steps: (1) selecting a theoretical energy-economy framework; (2) formulating modeling approaches; (3) econometrically fitting an APF to historical economic and energy data; (4) comparing and evaluating modeling approaches; (5) interpreting the economy; and (6) formulating energy and economic policy. We find that choices made in Steps 1–4 can lead to very different interpretations of the economy (Step 5) and policies (Step 6). To investigate these effects, we use empirical data (Portugal and UK) and the Constant Elasticity of Substitution (CES) APF to evaluate four modeling choices: (a) rejecting (or not) the cost-share principle; (b) including (or not) energy; (c) quality-adjusting (or not) factors of production; and (d) CES nesting structure. Thereafter, we discuss two revealing examples for which different upstream modeling choices lead to very different policies. In the first example, the (kl)e nesting structure implies significant investment in energy, while other nesting structures suggest otherwise. In the second example, unadjusted factors of production suggest balanced investment in labor and energy, while quality-adjusting suggests significant investment in labor over energy. Divergent outcomes provide cautionary tales for policymakers: greater understanding of upstream modeling choices and their downstream implications is needed.

Capital–labour–energy Constant Elasticity of Substitution (CES) production functions and their estimated parameters now form a key part of energy–economy models which inform energy and emissions policy. However, the collation and guidance as to the specification and estimation choices involved with such energy-extended CES functions is disparate. This risks poorly specified and estimated CES functions, with knock-on implications for downstream energy–economic models and climate policy. In response, as a first step, this paper assembles in one place the major considerations involved in the empirical estimation of these CES functions. Discussions of the choices and their implications lead to recommendations for CES empiricists. The extensive bibliography allows those interested to dig deeper into any aspect of the CES parameter estimation process.

Energy return on investment (EROI) is a ratio of the energy obtained in relation to the energy used to extract/produce it. The EROI of fossil fuels is globally decreasing. What do the declining EROIs of energy sources imply for society as a whole? We answer this question by proposing a novel EROI measure that describes, through one parameter, the efficiency of a society in managing energy resources over time. Our comprehensive societal EROI measure was developed by (1) expanding the boundaries of the analysis up to the useful stage; (2) estimating the amount of energy embodied in the energy-converting capital; (3) considering non-conventional sources such as the muscle work of humans and draught animals; and (4) considering the influence of imported and exported energy. We computed the new EROI for Portugal as a case study. We find a considerably lower EROI value, at around 3, compared to those currently available, which is stable over a long-time range (1960–2014). This suggests an independence of EROI from economic growth. When estimated at the final stage, using conventional methods (i.e., without applying the four novelties here introduced), we find a declining societal EROI. Therefore, our results imply that the production of new and more efficient final-to-useful energy converting capital has historically kept societal EROI around a stable value by offsetting the effects of the changing returns of energy sources at the primary and final stages. This will be crucial in the successful transition to renewables.

The benefits of energy efficiency are recognized in multiple socio-economic spheres. Still, the quantitative impact on macroeconomic performance is not fully understood, as modeling tools are not thermodynamically consistent—failing to explicitly include the useful stage of energy flows and/or thermodynamic efficiencies in primary–final–useful energy transformations. Misspecification in the link between energy use and the economy underplays the role of energy use and efficiency in economic growth. In this work, we develop and implement the Macroeconometric Resource Consumption model for Portugal (MARCO-PT), 1960–2014. Based on the post-Keynesian framework developed for the United Kingdom (MARCO-UK), our model explicitly includes thermodynamic energy efficiency, extending the analysis to the useful stage of energy flows. The model’s stochastic equations are econometrically estimated. The historical influence of key variables—namely thermodynamic energy efficiency—on economic output is assessed through counterfactual simulations and computation of year-by-year output elasticities. The MARCO-PT model adequately describes the historical behavior of endogenous variables. Although its influence has decreased over time, thermodynamic efficiency has consistently been the major contributor to economic growth between 1960–2014, with an average output elasticity of 0.46. Total useful exergy is also a major contributing factor, with an average output elasticity of 0.29. Both have a higher influence than capital, labor, or other energy variables (final energy, prices). An adequate integration of thermodynamic efficiency is thus crucial for macroeconomic models.

Biodiversity loss is a global environmental concern, mainly driven by human-induced factors, encompassing both direct and indirect drivers. This study investigates the long-term relationship between either the Human Footprint Index (HFI), which measures the extent of human pressures (i.e., direct drivers), or the Gross Domestic Product (GDP), a measure of economic growth (i.e., indirect driver) and biodiversity change, using bird population trends as indicators. The analysis was based on time-series data for Portugal (2004–2023) aggregated at national and sub-national scales, representative of different socio-economic contexts. Multi-species indices were regressed against either the HFI or GDP using Autoregressive Distributed Lag (ARDL) to identify long-run relationships. Bird population trends varied by species group (common, agricultural, and forest birds) and socio-economic context underscoring the importance of sub-national assessments. The HFI and GDP had varying predictive value across species groups and socio-economic contexts, with the HFI showing greater consistency, particularly as a predictor for agricultural birds. While most models showed a negative association between species abundance and either the HFI or GDP, revealing a signal of socio-economic pressures on bird populations at sub-national scales, some models suggested mixed results, indicating that conservation policies must take local contexts into account.