Brightcon 2026, hackathon and courses

Life Cycle Assessment (LCA), both in theory and practice, has traditionally relied on a strict distinction between the environment and the economy. Weidema et al. (2018) proposed a conceptual shift by relaxing this boundary and unifying economic activities and environmental mechanisms within an extended techno-biosphere matrix. In this formulation, environmental feedbacks can be represented in a mathematically consistent manner as input and output flows between environmental mechanisms and economic activities. Economic activities emit elementary flows that trigger environmental mechanisms, which in turn generate feedback flows to the technosphere, thereby closing the loop.
While this work opened the door to explicitly modeling feedback loops in LCA and brought the field closer to ecosystem services modeling, it has not yet been operationalized. A key limitation lies in the absence of guidance on how to quantify the flows returning from the environment to the economy, leaving the framework largely theoretical.
This contribution presents a brightway-based package, together with an extended conceptual and mathematical framework, designed to operationalize environmental feedbacks in LCA. A central feature of the approach is the explicit treatment of time, recognizing that environmental feedbacks unfold over extended temporal horizons. The implementation therefore builds on bw_timex (Müller et al., 2025) and premise (Sacchi et al., 2022) to propagate feedback mechanisms dynamically and prospectively. By representing the economy and environment as a single interacting system, the framework enables the construction of time-resolved characterized inventories, where elementary flows act as initial perturbations, analogous to functional unit demands in conventional LCA.
The framework provides a general structure to model feedbacks based on a set of inputs: (i) a feedback driver time pattern describing how an environmental variable evolves following an emission, (ii) a response pattern capturing how technosphere activities respond to this driver, and (iii) exogenous trajectories for both the driver and the demand of affected activities. For instance, feedbacks between CO₂ emissions and agricultural production via global temperature can be modeled using prospective and dynamic Global Temperature Potential (Barbosa Watanabe & Cherubini, 2026) to describe the temperature response, and global gridded crop models (Rosenzweig et al., 2014) to derive yield responses. An initial emission induces a temperature trajectory modification, which affects crop yields over time; when combined with projected demands for the affected crops (e.g., FAO scenarios), this results in additional emissions attributable to the initial perturbation. These new emissions induce a new temperature modification, leading to next iteration. The feedback is then iteratively propagated across time, with consistent alignment between prospective technosphere and biosphere dynamics.
A critical aspect of the framework is the separation of endogenous effects, i.e., those attributable to the initial emission, from exogenous influences driven by broader system dynamics. This distinction is rigorously implemented using partial derivatives within an adapted matrix-based LCA formulation, enabling consistent interaction among multiple feedback loops.
Overall, this work proposes a unified and operational framework for feedback modeling in LCA, bridging Life Cycle Inventory (LCI) and Life Cycle Impact Assessment (LCIA) within a dynamic and prospective perspective. By integrating feedbacks, temporal dynamics, and uncertainty propagation, it lays the foundation for a new research direction at the intersection of these domains. The modular design of the package supports detailed uncertainty analysis and scenario exploration. The open-source implementation is currently being tested on climate–agriculture feedbacks and interactions between particulate matter emissions and the healthcare sector.

Barbosa Watanabe, M. D., & Cherubini, F. (2026). Prospective Characterization Factors for Assessing Climate Change Impacts in Life Cycle Assessments. Environmental Science & Technology, acs.est.5c12391. https://doi.org/10.1021/acs.est.5c12391
Müller, A., Diepers, T., Jakobs, A., Cardellini, G., Von Der Assen, N., Guinée, J., & Steubing, B. (2025). Time-explicit life cycle assessment : A flexible framework for coherent consideration of temporal dynamics. The International Journal of Life Cycle Assessment. https://doi.org/10.1007/s11367-025-02539-3
Rosenzweig, C., Elliott, J., Deryng, D., Ruane, A. C., Müller, C., Arneth, A., Boote, K. J., Folberth, C., Glotter, M., Khabarov, N., Neumann, K., Piontek, F., Pugh, T. A. M., Schmid, E., Stehfest, E., Yang, H., & Jones, J. W. (2014). Assessing agricultural risks of climate change in the 21st century in a global gridded crop model intercomparison. Proceedings of the National Academy of Sciences, 111(9), 3268‑3273. https://doi.org/10.1073/pnas.1222463110
Sacchi, R., Terlouw, T., Siala, K., Dirnaichner, A., Bauer, C., Cox, B., Mutel, C., Daioglou, V., & Luderer, G. (2022). PRospective EnvironMental Impact asSEment ( premise ) : A streamlined approach to producing databases for prospective life cycle assessment using integrated assessment models IMAGE. Renewable and Sustainable Energy Reviews, 160(April 2021), 112311. https://doi.org/10.1016/j.rser.2022.112311
Weidema, B. P., Schmidt, J., Fantke, P., & Pauliuk, S. (2018). On the boundary between economy and environment in life cycle assessment. The International Journal of Life Cycle Assessment, 23(9), 1839‑1846. https://doi.org/10.1007/s11367-017-1398-4