Data-Driven Sustainability: Methods and Evidence Across Energy, Policy, and Industry

1. Introduction

Sustainable development increasingly depends on turning complex, heterogeneous data into reliable, decision-grade evidence. This article examines how data science, artificial intelligence, optimization, and system modeling are being used to advance environmental sustainability across fuels and power systems, policy and behavior, and industrial processes and infrastructure. We synthesize methods and results from thirteen studies, identify cross-cutting information on data quality and model credibility, and outline priorities for reproducible workflows, open datasets, and decision support that couples technical metrics with costs and policy constraints.

The acceleration of decarbonization, resource constraints, and environmental conditions demand transparent, timely, and justified decisions. To achieve this, heterogeneous data from sensors, experiments, and operations should be transformed into decision-grade evidence coupled with models that have explicit assumptions and uncertainties. Advances in data acquisition, computing, and analytics have enabled multi-scale learning, ranging from process units and production lines to power systems and policy portfolios.

This article synthesizes methods and results that illustrate how data science, artificial intelligence, optimization, and system modeling can improve environmental outcomes across four domains: fuels and power systems, policy and behavior, industrial processes, and infrastructure. Its scope spans supervised learning and forecasting, physics-informed and mechanistic models, lifecycle assessment and multicriteria decision analysis, and efficiency benchmarking. Emphasis is placed on model credibility (data quality, validation, sensitivity) and reproducibility (clear workflows, comparable metrics), alongside the transferability of insights beyond individual case studies. While examples are taken from thirteen studies, the objective is a general, cross-sector account of the methods and evidence behind data-driven sustainability.

2. Forecasting, Optimization, and AI Models

As modern energy systems are increasingly characterized by volatility, decentralization, and data richness, more sophisticated approaches to forecasting, control, and decision-making are required. Within this context, several contributions in this Special Issue demonstrate how machine learning and optimization techniques can enhance performance and adaptability in energy applications.

One study investigates lignin hydrogenolysis, a crucial process in the valorization of biomass, by applying supervised machine learning models to map chemical process parameters to desirable outcomes [1]. By training algorithms on experimental datasets, the authors identify key operational variables significantly influencing product yield, such as the lignin-to-solvent ratio and catalyst pore size. Their results not only improve our mechanistic understanding of lignin breakdown but also present an empirical pathway for optimizing future bio-refinery designs.

Regarding power system operations, another paper focuses on short-term load forecasting using a range of regression techniques, including decision trees and support vector machines [2]. The authors employ hyperparameter tuning methods such as grid search and Bayesian optimization to refine the model’s performance, achieving significant reductions in the mean square error. This work offers valuable insights for system operators tasked with maintaining grid reliability under fluctuating demand conditions.

The third contribution instead focuses on solar thermal energy, presenting a physics-based simulation of a parabolic trough collector (PTC) [3]. The study compares the performance of different heat transfer fluids under varying solar disturbance profiles and control strategies. By quantifying thermal efficiency and operational robustness, the paper provides guidance for improving the stability and cost-effectiveness of solar thermal power plants.

Collectively, these studies exemplify how data-driven and model-based techniques can augment system intelligence across different stages of the energy value chain, from molecular-scale reactions to national grid forecasting and solar plant optimization.

3. Energy Policy and Behavior

The transition to sustainable energy systems is deeply embedded in socio-economic structures and public policy frameworks. Technology alone cannot drive transformation; rather, policies, pricing instruments, and behavioral responses play a decisive role in shaping outcomes. The contributions in this section examine how energy policies, particularly those rooted in pricing mechanisms and performance evaluation, affect both individual and systemic behavior across diverse geographical contexts.

In a compelling case study from Saudi Arabia, electricity consumption data from residential households are analyzed before and after the implementation of revised tariff structures [4]. By correlating changes in consumption with household characteristics, such as dwelling type, ventilation, and occupancy, the study reveals that the tariff reforms led to a statistically significant decline in electricity use. These findings underscore how targeted policy changes, even in regions with traditionally high per capita consumption, can influence energy-conscious behavior.

Zooming out to a continental scale, another study applies a novel Temporal PROSA method, a dynamic multicriteria decision-making approach, to assess the renewable energy transition across EU member states between 2004 and 2021 [5]. This method accounts for both temporal dynamics and the aggregation of performance over time, offering a nuanced ranking of national progress. The analysis identifies Sweden and Portugal as long-standing leaders, while countries like Poland, Cyprus, and Luxembourg are found to have lagged in their renewable energy uptake. This work exemplifies the value of integrated decision-support tools for cross-country policy benchmarking.

A third paper introduces a data-driven internal carbon pricing mechanism to optimize wood procurement in Finland’s integrated energy and materials industry [6]. By factoring in carbon sinks, EU emission allowance prices, and transport distances, the proposed mechanism delivers improved economic outcomes while maintaining carbon neutrality. The study highlights how internal carbon pricing can evolve beyond a compliance tool into a strategic asset for industrial decision-making under sustainability constraints.

These contributions emphasize that effective policy design, grounded in empirical data and behavioral insight, is essential for accelerating sustainable energy transitions and ensuring that economic findings align with environmental objectives.

4. Sustainability in Industrial Processes and Infrastructure

While sustainability discourse often centers on energy generation, the processes and infrastructures that support manufacturing, agriculture, and material transformation are equally important. These domains present significant opportunities for reducing energy intensity, minimizing waste, and enhancing systemic efficiency. The papers in this category highlight how data-driven methodologies can yield actionable insights across diverse sectors, ranging from metallurgy to irrigation.

One contribution explores the intersection of quality control and energy efficiency in casting processes, proposing a composite evaluation model to quantify the effectiveness and energy consumption of various defect detection methods [7]. Through comparative analysis, X-ray testing is found to balance cost, accuracy, and energy use most efficiently. This integrative approach demonstrates how operational decisions in manufacturing can be informed by broader sustainability considerations.

At the product design level, another study introduces an extended QFD-CE (Quality Function Deployment–Circular Economy) framework that integrates sustainability metrics into the early phases of development [8]. This method is applied to photovoltaic panels and incorporates techniques such as the SMARTER method, brainstorming, and environmental impact weighting. By embedding circular economy goals into technical design workflows, it offers a replicable model for sustainable innovation.

In the field of metallurgy, a comprehensive mathematical model is developed to simulate aluminum production in Soderberg electrolytic cells [9]. This model captures thermal and electromagnetic dynamics while incorporating system-level parameters, optimizing energy use in primary aluminum production. This study stands out for bridging theoretical modeling with practical metallurgical challenges.

Finally, in the field of water infrastructure, one of the studies applies data envelopment analysis (DEA) to assess the operational efficiency of irrigation canals in Jharkhand, India [10]. By benchmarking nine water user associations based on input–output efficiency scores, the analysis identifies both high-performing and underperforming systems, providing clear policy implications for enhancing water management in resource-constrained agricultural regions.

Together, these papers demonstrate how applying data analytics and systems thinking to industrial and infrastructural challenges can lead to substantial environmental and economic benefits.

5. Bioenergy and Sustainable Fuels

A significant area of sustainability-focused research resolves around low-carbon alternatives to fossil fuels, particularly those derived from biomass and organic waste. This Special Issue features three contributions exploring diverse technological pathways for biofuel, each addressing critical challenges in feedstock utilization, process efficiency, and lifecycle performance.

In the context of bioethanol production, one study presents a solid-state fermentation strategy using Aspergillus niger and A. flavus to break down lignocellulosic biomass under immobilized conditions [11]. This research not only optimizes enzyme activity across various fermentation parameters but also demonstrates the superior ethanol yields and stability achieved through immobilized yeast systems.

Complementing this work, another contribution evaluates bio-methanol production routes enabled by the CONVERGE technology platform [12]. Using lifecycle assessment (LCA) and scenario analysis, the authors benchmark the environmental impacts of different biomass sources and process configurations. Their findings highlight the comparative advantages of wooden biomass over olive pomace for most sustainability indicators, reinforcing the role of advanced process design in decarbonizing chemical production.

With regard to aviation fuels, a third study applies the TOPSIS multicriteria method to assess four competing technological pathways for sustainable aviation fuel (SAF) in China [13]. By combining the projected SAF demand with technical development and carbon abatement potential, the analysis demonstrates that hydroprocessed esters and fatty acids (HEFAs) will be most suitable in the short term, while power-to-liquid (PtL) fuels will become optimal in longer-term scenarios. This structured evaluation provides a valuable decision-support framework for aligning technology investment with national policy goals.

Together, these papers underscore the versatility of biomass as a renewable feedstock and the importance of rigorous data-driven evaluation in selecting viable fuel alternatives.

6. Reflections and Outlook

Across the thirteen studies synthesized here, a consistent finding emerges: credible progress in environmental sustainability depends on fit-for-purpose data, transparent evaluation, and the optimal coupling of data-driven methods with mechanistic insight. When models make their assumptions explicit, quantify uncertainty, and report problem-relevant metrics, they not only improve predictive or optimization performance but also strengthen the link between analysis and operational and policy decisions.

The current evidence base remains fragmented, frequently comprising single-site and method-specific studies using heterogeneous metrics, which limits comparability and generalization. Future research in this field should prioritize the following: open benchmarks with standardized targets and train/validation splits; harmonized reporting of lifecycle boundaries and system assumptions; routine uncertainty propagation and sensitivity analysis; and wider external validation across geographies and operating regimes. Methodologically, there is clear value in physics-informed and hybrid learning, causal designs that move beyond correlation, and decision support that combines technical performance with costs, constraints, and distributional impacts. Practically, reproducible pipelines and shared datasets can accelerate the transition from labs and pilots to deployment. These advancements will both enhance the accuracy of data-driven sustainability and render it more actionable, equitable, and robust to the real-world disturbances that shape energy, industry, and infrastructure systems.