Search Results (38)

Electric vehicle(EV) diffusion exhibits nonlinear, path-dependent dynamics shaped by interacting economic, technological, and social constraints. This paper develops a unified hybrid systems framework that captures these complexities by integrating microfounded household choice, capacity-constrained firm behavior, local network spillovers, and multi-level policy intervention within a Filippov differential-inclusion structure. Households face heterogeneous preferences, liquidity limits, and network-mediated moral and informational influences; firms invest irreversibly under learning-by-doing and profitability thresholds; and national and local governments implement distinct financial and infrastructure policies subject to budget constraints. The resulting aggregate adoption dynamics feature endogenous switching, sliding modes at economic bottlenecks, network-amplified tipping, and hysteresis arising from irreversible investment. We establish conditions for the existence of Filippov solutions, derive network-dependent tipping thresholds, characterize sliding regimes at capacity and liquidity constraints, and show how network structure magnifies hysteresis and shapes the effectiveness of local versus national policy. Optimal-control analysis further demonstrates that national subsidies follow bang–bang patterns and that network-targeted local interventions minimize the fiscal cost of achieving regional tipping. Beyond theoretical characterization, the framework is structurally calibrated to match the order-of-magnitude effects reported in leading empirical and simulation-based studies, including network diffusion models, agent-based simulations, bass-type specifications, and fuel-price shock analyses. The hybrid formulation reproduces short-run percentage-point subsidy effects, long-run forecast dispersion under alternative network assumptions, and policy-induced equilibrium shifts observed in the applied literature while providing a unified geometric interpretation of these heterogeneous results through explicit basin boundaries and regime switching. The framework provides a complex systems perspective on sustainable mobility transitions and clarifies why identical national policies can generate asynchronous regional outcomes. These results offer theoretical foundations for designing coordinated, cost-effective, and network-aware EV transition strategies. Full article

The transition toward sustainable energy in Arctic and subarctic regions requires innovative approaches that account for both the unique geographical conditions and the economic and policy challenges associated with isolated power systems. This study examines how net energy metering (NEM) and net billing schemes influence distributed solar photovoltaic (PV) adoption and financial performance among utilities in Alaska’s Railbelt. The Railbelt, which supplies power to three-quarters of the state’s population, remains heavily reliant on natural gas and exhibits limited renewable penetration compared to other arctic regions. Using a stochastic risk-based modeling framework with Monte Carlo simulations and the Bass diffusion model, the analysis estimates the 15-year financial impacts of different NEM adoption scenarios on utilities. Results show that while NEM drives PV adoption through higher compensation for exported generation, it also increases potential revenue losses for utilities compared to net billing. Policy innovations like those introduced in Alaska’s House Bill 164 (HB 164), which establishes a reimbursement fund to mitigate utility revenue losses, indicate that regulatory work is being designed to balance distributed generation incentives with economic sustainability. This work provides a baseline for understanding how a policy framework influences both utility and consumer economics in terms of NEM and solar PV adoption in Arctic and subarctic systems. Full article

The world is increasingly confronted with interconnected challenges such as energy shortages and climate change. Fossil fuels, including coal, oil, and natural gas, remain the dominant global energy sources, yet they are major contributors to greenhouse gas emissions and growing geopolitical instability. In response to energy insecurity and environmental pressures, many countries are expanding their use of renewable energy sources, including hydropower, solar, wind, and geothermal. Hydropower currently generates more electricity than all other renewable technologies combined and is expected to remain the largest source of renewable electricity through the 2030s. This paper analyzes the role of hydropower in national energy transitions by applying innovation diffusion models. Using an innovation diffusion framework, via the Bass Model, we examine the dynamics of hydropower generation across multiple countries and find that this approach effectively captures the mean nonlinear trajectory of most countries. We complete the analysis by evaluating the effect of rainfall on hydropower generation and show that this helps capture the residual variability not modeled by the Bass Model. Full article

Household adoption of residential photovoltaic systems in community microgrids is shaped by economic, infrastructural, and social factors. Previous studies have shown that agent-based modelling can help analyse adoption, but it often lacks clear mathematical foundations, systematic validation, and reproducibility. This study presents an open-source agent-based model with two advances: (1) a fuzzy-utility method for household decision-making and (2) combined modelling of financial incentives, grid reliability, infrastructure access, and peer effects as adoption drivers. The model explores adoption under diverse policy and technical scenarios, validates results against Bass diffusion and discrete choice models, and applies a Sobol-based sensitivity analysis to identify key parameters. Results clarify how incentives, barriers, and social influence shape adoption trajectories. By demonstrating cost-sharing dynamics and peer network effects and openly sharing model code and data, this study provides a transparent and reproducible benchmark for future community microgrid research. Full article

A system dynamics approach is described to explore the path of Carbon Capture diffusion. The proposed model, in principle, follows the Bass diffusion of innovation theory and includes all major influencing factors. The primary contribution of this paper is the modification of Bass’s model to reflect parameters affecting the adoption of Carbon capture and storage technology. Consequently, it differs from other extensions to Bass’s model. The underpinning of this work is the system dynamics (SD) approach, which can open a pathway for further research into CCS acceptance. The proposed model’s behaviour is illustrated for various transition pathways of the technology, for different regimes. By modifying the proposed model, the paper also allows consideration of various capturing technologies on their merit. The proposed framework enables the examination of the impact of intervention policies on the adoption of CCS by individual investors. The purpose is to identify the parameters of these policies to support the under-resourced CCS technology and reduce the need for government participation. It is worth noting that the SD is primarily a descriptive method used for scenario analysis to illustrate what the future would look like. Full article

The adoption of telework increased as a sustainable strategy after the COVID-19 pandemic. However, its impact on transportation and energy consumption are controversial, emphasizing the need for context-specific analysis. This research developed a System Dynamics (SD) simulation that integrated the generalized Bass Diffusion Model (BDM) and Technology Acceptance Model (TAM) to analyze telework diffusion in organizations and its influence on transport-related CO₂ emissions and energy consumption in Colombia. Internal conditions, particularly managerial attitudes and perceptions of telework performance, play a crucial role in the adoption rate. Telework adoption follows a weak S-curve pattern primarily driven by internal dynamics rather than external pressures, lagging behind the projections set by public policies and global trends. Simulations based on government data for the period 2012–2022 indicated that the number of teleworkers could reach 1.61 million by 2032, resulting in annual energy savings of approximately 1.5% and a 2% reduction in transport-related CO₂ emissions. Sustained governmental tracking of sectoral adoption and including records of household energy use will support sensitivity analysis and strengthen model robustness. The integrated SD, TAM, and BDM modeling approach identified critical factors to boost telework adoption and its environmental benefits, providing insights for sustainable organizational strategies and public policies. Full article

This work proposes a hybrid model that combines the Galam model of opinion dynamics with the Bass diffusion model used in technology adoption on Barabasi–Albert complex networks. The main idea is to advance a version of the Bass model that can suitably describe an opinion formation context while introducing irreversible transitions from group B (opponents) to group A (supporters). Moreover, we extend the model to take into account the presence of a charismatic competitor, which fosters conversion back to the old technology. The approach is different from the introduction of a mean field due to the interactions driven by the network structure. Additionally, we introduce the Kolmogorov–Sinai entropy to quantify the system’s unpredictability and information loss over time. The results show an increase in the regularity of the trajectories as the preferential attachment parameter increases. Full article

Accurate forecasting of Distributed Energy Resource (DER) adoption is essential for decarbonization, effective policy, and infrastructure planning. This paper develops a comparative framework integrating trend-based and goal-oriented approaches using the logistic growth and Bass diffusion models. Using Danish household data for electric vehicles (EVs), heat pumps (HPs), and rooftop photovoltaics (PVs), we evaluate four logistic-growth-based and two Bass-diffusion-based methods. Each method supports standard curve-fitting (trend-based) or incorporates explicit policy goals (goal-based), such as reaching a specified adoption threshold by a target year. An integrated flow diagram visually summarizes the decision process for method selection based on data availability, market maturity, and policy targets. Results show that Bass diffusion excels in early-stage or policy-driven markets like EVs, while logistic approaches perform better for PVs after subsidies are removed, with HP adoption falling in between. A key innovation is integrating future adoption targets into parameter estimation, enabling stakeholders to assess the required acceleration in adoption rates. The findings highlight the need to align model choice with data, market conditions, and policy objectives, offering practical guidance to accelerate DER deployment. Full article

Background/Objectives: This study examines the longitudinal dynamics of anti-nucleocapsid (anti-N) and anti-spike (anti-S) antibody responses to SARS-CoV-2 infection and mRNA vaccination based on 81,878 serum samples from 23,616 healthcare workers (HCWs) across five European countries. It includes data across four scheduled vaccine doses—predominantly BNT162b2—with 25% of samples originating from individuals with confirmed prior infection, as evidenced by elevated anti-S levels, positive Anti-N antibodies, or PCR results. Methods: The study employed a shifted transformation method for data normalization and utilized the Bass diffusion model to predict antibody titer dynamics influenced by both internal factors—such as immune activation contextualized through sociodemographic issues—and external factors, including infection and vaccination. Despite the absence of direct measurements for some internal variables, the model effectively inferred their impact, enabling a rigorous and nuanced delineation of immune response profiles. Results: The Bass diffusion model rigorously captured variations in antibody titers, analyzed through demographic factors such as gender, age, and job role, while thoroughly accounting for pre-infection status. The results indicate that Anti-N antibodies, exclusively produced post-infection, exhibited a rapid decline, while anti-S antibodies, generated from both infection and vaccination, demonstrated prolonged persistence. A significant decline in anti-S levels was observed 3–5 months post-vaccination, with adaptive immunity—characterized by the dominance of internal factors effects relative to external ones—achieved in most groups after the fourth dose. However, adaptive immunity post second dose was limited to specific demographics. Conclusions: These findings emphasize the significance of the Bass Method in predicting vaccine-induced, hybrid immune responses and detecting adaptive immunity by overcoming limitations in internal factor data, thereby advancing effective vaccination and infection control strategies during public health crises. These findings highlight the Bass Method’s value in predicting vaccine-induced and hybrid immunity, effectively addressing internal factor data gaps to enhance vaccination and infection control strategies. Full article

Hydroelectric power is one of the most important renewable energy sources in the world. It currently generates more electricity than all other renewable technologies combined and, according to the International Energy Agency, it is expected to remain the world’s largest source of renewable electricity generation into the 2030s. Thus, despite the increasing focus on more recent energy technologies, such as solar and wind power, it will continue to play a critical role in energy transition. The management of hydropower plants and future planning should be ensured through careful planning based on the suitable forecasting of the future of this energy source. Starting from these considerations, in this paper, we examine the evolution of hydropower with a forecasting analysis for a selected group of countries. We analyze the time-series data of hydropower generation from 1965 to 2023 and apply Innovation Diffusion Models, as well as other models such as Prophet and ARIMA, for comparison. The models are evaluated for different geographical regions, namely the North, South, and Central American countries, the European countries, and the Middle East with Asian countries, to determine their effectiveness in predicting trends in hydropower generation. The models’ accuracy is assessed using Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE). Through this analysis, we find that, on average, the GGM outperforms the Prophet and ARIMA models, and is more accurate than the Bass model. This study underscores the critical role of precise forecasting in energy planning and suggests further research to validate these results and explore other factors influencing the future of hydroelectric generation. Full article

This study presents a mathematical model for forecasting the development of Ukraine’s Integrated Power System (IPS) until 2040, with a specific focus on the expansion of nuclear energy as a cornerstone of the nation’s low-carbon transition. The model is an extension of Frank Bass’s mixed influence diffusion model, incorporating both economic and technological factors. These factors are treated as stochastic variables essential for accurately predicting the evolution of an integrated energy system, particularly in the context of rapid renewable energy sources (RES) growth. The model employs regression techniques using generalized logistic curves, improving forecasting efficiency by aligning modeling parameters with experimental data. The study’s results indicate the potential for optimizing IPS components, including nuclear and thermal power generation, through the model’s application. The model is distinguished by its inclusion of economic and technological impacts, such as state matrices, control actions, and external influence matrices, which enhance the accuracy of simulations and predictions. The validation of the model, based on scenarios of electricity consumption and generation, shows significant alignment with observed trends, confirming the model’s reliability. The findings suggest that this model is an effective tool for developing and refining energy system scenarios, with nuclear energy playing a pivotal role in Ukraine’s sustainable energy future. Full article

This paper examines the role of hydropower in the context of the energy transition, using innovation diffusion models. The study analyzes time series data of hydropower generation from 1965 to 2022 by applying diffusion models and some other models, such as Prophet and ARIMA, for comparison purposes. The models are evaluated across diverse geographic regions, including America, Africa, Europe, Asia, and the Middle East, to determine their effectiveness in predicting hydropower generation trends. The analysis reveals that the GGM consistently outperforms other models in accuracy across all regions. In most cases, the GGM exhibits better performance compared to the Bass, ARIMA, and Prophet models, highlighting its potential as a robust forecasting tool for hydropower generation. This study emphasizes the critical role of accurate forecasting in energy planning and calls for further research to validate these findings and explore additional factors influencing hydropower generation evolution. Full article

Dubai, a popular vacation spot, has launched an initiative to reduce reliance on single-use plastic water bottles. Tourists in Dubai widely utilize PET (Polyethylene Terephthalate) water bottles, and significant quantities of greenhouse gases (GHG) are released during the production and disposal of PET bottles. In response to Dubai’s initiative, some hotels eliminated PET bottles and substituted them with environmentally favorable alternatives. These hotels are considered adopters of the initiative, while other hotels that might follow are imitators. Thus, innovation diffusion theory (IDT) is used in this work to forecast the transition of hotels to non-PET bottles. The diffusion of this new behavior is simulated using a system dynamic (SD) model, where factors pushing imitators to abolish PET bottles are found using the Delphi method and hotel surveying. Moreover, the importance of each identified factor is found using an analytical hierarchical process (AHP). Since hotels are divided into several categories based on their service quality, the analysis shows that hotels are affected by other hotels in their category or better categories. Using this conceptual understanding, Bass and generalized Bass modeling are used in the SD model to study how imitating hotels will follow the adopters. Best-, average-, and worst-case scenarios are studied to help decision-makers understand what to expect in the future. For the best- and average-case scenarios, the SD simulation shows that all hotels will potentially have abolished PET bottles in 25 years. However, only 16% of hotels will have cancelled PET bottles in 25 years if the worst-case scenario occurs; thus, decision-makers need to intervene to expedite the process. Full article

By collecting and expanding several numerical recipes developed in previous work, we implement an object-oriented Python code, based on the networkX library, for the realization of the configuration model and Newman rewiring. The software can be applied to any kind of network and “target” correlations, but it is tested with focus on scale-free networks and assortative correlations. In order to generate the degree sequence we use the method of “random hubs”, which gives networks with minimal fluctuations. For the assortative rewiring we use the simple Vazquez-Weigt matrix as a test in the case of random networks; since it does not appear to be effective in the case of scale-free networks, we subsequently turn to another recipe which generates matrices with decreasing off-diagonal elements. The rewiring procedure is also important at the theoretical level, in order to test which types of statistically acceptable correlations can actually be realized in concrete networks. From the point of view of applications, its main use is in the construction of correlated networks for the solution of dynamical or diffusion processes through an analysis of the evolution of single nodes, i.e., beyond the Heterogeneous Mean Field approximation. As an example, we report on an application to the Bass diffusion model, with calculations of the time $t_{max}$ of the diffusion peak. The same networks can additionally be exported in environments for agent-based simulations like NetLogo. Full article

In addition to passenger vehicles, battery-electric trucks and buses could offer substantial flexibility to the energy system. Using a Bass diffusion model, we extrapolated the unidirectional charging needs and availability of trucks in five of eleven typical applications, as well as city buses, for Germany until 2040. Combined, these heavy-duty vehicles could provide up to 23 GW of down-regulating flexibility potential (i.e., in case of excess power supply) in 2040. The resulting revenues could contribute to reducing electricity costs for depot operators. These results illustrate the need to provide easy and automated market access to heavy-duty vehicle fleets. Full article