Aaron Praktiknjo

Sustainable energy systems require major electricity grid expansion, yet the environmental impacts of future grid infrastructure are largely overlooked. We assess these impacts by combining a detailed model of Germany’s grid infrastructure with Integrated Assessment Model scenarios reflecting future supply chains. While more renewables help decarbonize grid component production, transmitting and distributing this renewable electricity requires additional grid expansion, raising emissions. We find that grid infrastructure becomes a major contributor to climate impacts of future electricity, with shares rising from 1% in 2023 to up to 21% by 2045. Main drivers of grid-related impacts, even under ambitious decarbonization, remain electricity and heat generation for aluminum production alongside emissions from steelmaking. Moreover, decarbonizing supply chains shift environmental impacts towards land use and mineral depletion. Our findings underscore the growing environmental relevance of grid infrastructure, reveal levers for reducing grid-related impacts, and call for their integration into global policy and energy planning.

Natural gas supplies in Europe were disrupted and energy prices soared in the context of Russia’s invasion of Ukraine. Electricity prices in France experienced the largest relative increase among European countries, even though the share of natural gas in the electricity mix is small compared to its neighbours. In this article, we demonstrate the importance of causal statistical methods and propose causal graphs to investigate the French and Spanish electricity markets and pinpoint key influencing factors on electricity prices and net exports. We demonstrate that a causal approach resolves paradoxical results of simple correlation studies and enables a quantitative analysis of indirect causal effects and what-if scenarios. We introduce a linear structural causal model as well as non-linear tree-based machine learning combined with Shapley Flow values. The models elucidate the interplay of gas prices and the unavailability of nuclear power plants during the energy crisis as the high unavailability made France dependent on imports.

Industrialized countries such as Germany continue to struggle to meet their greenhouse gas (GHG) reduction targets, underscoring the need for a clear understanding of the structural forces shaping emission trends. This study applies Structural Decomposition Analysis (SDA) to examine the drivers and inhibitors of Germany’s GHG emissions from 1996 to 2022, using the newly developed Python tool TOAD (Trade-Oriented Analysis through Decomposition). TOAD applies the Dietzenbacher and Los (Economic Systems Research, 10:307–324, 1998) method across four accounting schemes: territorial-based, consumption-based, production-based, and a trade-balance-adjusted perspective. Across perspectives, improvements in production efficiency and cleaner technologies emerge as the dominant source of emission reductions (− 617 Mt CO ₂ -eq), while growth in the scale of final demand remains the main driver of increases (+ 309 Mt CO ₂ -eq). Under a trade-balance-adjusted perspective, however, net reductions amount to only—38 Mt CO ₂ -eq, as rising emissions embodied in imports substantially offset domestic efficiency gains. These findings demonstrate that Germany’s apparent mitigation progress critically depends on accounting for trade-related outsourcing. A sectoral breakdown further reveals pronounced heterogeneity in the underlying emission drivers: energy-intensive industries are shaped by structural and activity-related rebound effects, whereas electricity and service sectors benefit most from intensity-driven improvements. These sector-specific dynamics remain hidden in aggregate SDA results. Overall, the study shows that Germany’s mitigation performance can only be fully understood when decomposed across multiple allocation principles and sectoral contexts. By enabling transparent and reproducible multi-perspective decomposition analyses, TOAD highlights the growing influence of consumption-driven demand, trade integration, and sector-specific rebound risks - insights that can inform more targeted climate and industrial policy.

The electrification of mobility increases the need for efficient local distribution and charging infrastructures. In this context, direct current (DC) architectures may reduce conversion stages, transmission losses, and material demand compared with alternating current (AC) systems. This study aims to quantify the environmental implications of AC- and DC-based grid and charging infrastructures for large-scale rollout in Germany. For this purpose, a dynamic life-cycle assessment (DLCA) is conducted for parking garages, parcel centers, and delivery bases over the period 2023–2045, covering the production and use phases with respect to global warming potential (GWP) and material demand. The results show that DC configurations achieve lower total GWP across all application contexts investigated. For parking garages, DC reduces total GWP by 9.3% compared with AC, while for parcel logistics facilities the reduction amounts to 5.7%. Copper is identified as the dominant material driver, and DC reduces copper demand by 17.1–58.7% depending on the application. A screening-based supply-risk assessment further indicates the elevated relevance of copper due to rising demand and Germany’s import dependence. Overall, the findings provide quantitative evidence that DC-based infrastructures can reduce both environmental impacts and copper demand in large-scale charging infrastructure deployment.