Technology Diffusion Model Research Articles

Renewable energy technology diffusion model: Evaluation of financial incentives in the electricity market of ecuador

This paper develops a simulation model using the System Dynamics paradigm to evaluate the effect of applying financial incentives on the diffusion of non-conventional renewable energy technologies in the Ecuadorian electricity market. The barriers to the diffusion of renewable energies are studied, the diffusion models are characterised, and the model is built with market variables and financial indicators integrated into its base structure. The Ecuadorian electricity market is described as a case study focusing on the incentive scheme. Based on investment, generation, and indirect incentives, this scheme plays a crucial role in promoting renewable energy technologies. Different long-term diffusion scenarios are simulated and compared with each other and with the baseline scenario to obtain diffusion rates and financial information for five renewable generation technologies: non-conventional hydro, wind, solar photovoltaic, biomass, and geothermal. The results of the simulations show that the combination of incentives leads to high diffusion rates, reassuring the audience about the potential impact of the proposed model. However, the feed-in tariff and tradable green certificate incentives are the most effective in promoting the use of renewable energy. For the Ecuadorian electricity market, the results show that wind and biomass technologies would be the most profitable, as opposed to geothermal energy, whose diffusion will not be feasible within the simulated period.

Simulation Analysis of Artificial Intelligence Technology Diffusion under Market Competition and Policy Incentives Based on Complex Network Evolutionary Game Models

The relationship network between enterprises will change their adoption behavior of AI technology and this micro-decision-making mechanism will eventually decide whether AI technology can diffuse and the extent of diffusion on the macro level. However, the existing AI technology diffusion research mostly focuses on the integration of AI technology with other industries from the industrial level, ignoring the complexity of the micro-complex game process and interactions within the enterprise network on the macro technology diffusion. In this regard, this paper builds a game model of AI technology diffusion in core and non-core enterprises from the levels of market competition and policy incentives based on complex network evolutionary game theory. It does this through simulation analysis that examines the mechanism of key factors affecting the diffusion of AI technology, as well as the influence and combination effects of pertinent policies. The study shows that (1) AI technology diffuses more effectively in non-core enterprises than it does in core enterprises; (2) changes in parameters like technology cost and policy regimes have a more evident impact on core enterprises than non-core ones; (3) in market competition, increasing the network average degree, the proportion of AI technology products in the mainstream market, the opportunity cost, the cost reduction factor, or decreasing the cost of AI technology can all promote the diffusion of AI technology; (4) under policy incentives, increasing the proportion of AI technology subsidies and the influence of high-tech identification of enterprises can both promote the diffusion of AI technology.

Green technology diffusion model and simulation considering enterprise technology absorption capacity

ABSTRACT The diffusion and wide application of green technologies are important means to achieve climate change mitigation. Concerning the green technology absorption capacity and technology diffusion willingness of enterprises, this paper divided enterprises into four categories, constructed a green technology diffusion model and analysed the equilibrium point and stability of the model. Then, we studied the diffusion process and laws of green technology through simulation experiments. The results show that in the green technology diffusion system, the basic regeneration number is the key factor in determining the equilibrium state of green technology diffusion. The disease-free equilibrium point of the green technology diffusion system is globally asymptotically stable, and the endemic equilibrium point is locally asymptotically stable. In order to improve the speed and scale of green technology diffusion, it is necessary to create green technology diffusion sources through multiple channels, improve enterprise technology absorption capacity through multiple methods, and enhance enterprise green technology diffusion willingness at multiple levels.

Epidemic effects in the diffusion of emerging digital technologies: evidence from artificial intelligence adoption

The properties of emerging, digital, general-purpose technologies make it hard to observe their adoption by firms and identify the salient determinants of adoption. However, these aspects are critical since the patterns related to early-stage diffusion establish path-dependencies which have implications for the distribution of the technological opportunities and socio-economic returns linked to these technologies. We focus on the case of artificial intelligence (AI) and train a transformer language model to identify firm-level AI adoption using textual data from over 1.1 million websites and constructing a hyperlink network that includes >380,000 firms in Germany, Austria, and Switzerland. We use these data to expand and test epidemic models of inter-firm technology diffusion by integrating the concepts of social capital and network embeddedness. We find that AI adoption is related to three epidemic effect mechanisms: 1) Indirect co-location in industrial and regional hot-spots associated to production of AI knowledge; 2) Direct exposure to sources transmitting deep AI knowledge; 3) Relational embeddedness in the AI knowledge network. The pattern of adoption identified is highly clustered and features a rather closed system of AI adopters which is likely to hinder its broader diffusion. This has implications for policy which should facilitate diffusion beyond localized clusters of expertise. Our findings also point to the need to employ a systemic perspective to investigate the relation between AI adoption and firm performance to identify whether appropriation of the benefits of AI depends on network position and social capital.

Probabilistic projections of granular energy technology diffusion at subnational level.

Projections of granular energy technology diffusion can support decision-making on climate mitigation policies and infrastructure investments. However, such projections often do not account for uncertainties and have low spatial resolution. S-curve models of technology diffusion are widely used to project future installations, but the results of the different models can vary significantly. We propose a method to create probabilistic projections of granular energy technology diffusion at subnational level based on historical time series data and testing how various projection models perform in terms of accuracy and uncertainty to inform the choice of models. As a case study, we investigate the growth of solar photovoltaics, heat pumps, and battery electric vehicles at municipality level throughout Switzerland in 2000-2021 (testing) and until 2050 (projections). Consistently for all S-curve models and technologies, we find that the medians of the probabilistic projections anticipate the diffusion of the technologies more accurately than the respective deterministic projections. While accuracy and probabilistic density intervals of the models vary across technologies, municipalities, and years, Bertalanffy and two versions of the generalized Richards model estimate the future diffusion with higher accuracy and sharpness than logistic, Gompertz, and Bass models. The results also highlight that all models come with trade-offs and eventually a combination of models with weights is needed. Based on these weighted probabilistic projections, we show that, given the current dynamics of diffusion in solar photovoltaics, heat pumps, and battery electric vehicles in Switzerland, the net-zero emissions target would be missed by 2050 with high certainty.

Simulating the CCUS technology diffusion in thermal power plants: An agent-based evolutionary game model in complex networks

The utilization of carbon capture, utilization, and storage (CCUS) technology is critical for achieving carbon neutrality and establishing a circular carbon economy. Thermal power plants represent a significant contributor to carbon emissions, and facilitating the spread of CCUS technology throughout the power industry is a crucial means of advancing this technology. The diffusion of CCUS technology would be influenced by policy incentives, market environment, enterprises' individual characteristics as well as complex social network relationships. To study the impacts of these factors on the diffusion of CCUS technology, this study develops an evolutionary game model of CCUS technology diffusion in thermal power plants based on a small-world network. This model includes agents as the network nodes with different production capacities and decision preferences. Through numerical simulations, the study examined the impact of market prices, carbon taxes, and subsidies as incentive measures under different diffusion environments. The results demonstrate that those with higher production capabilities show a higher level of sensitivity to incentives for technology diffusion. To reduce fiscal expenditure, the government can adopt differentiated incentive measures for enterprises with varying production capacities. In addition, the synergistic combination of various incentive measures can effectively decrease the government's expenses in promoting CCUS technology. The incentive mechanisms for promoting CCUS diffusion should be implemented preferably during the early stage of low-carbon transition in the power sector. As the operating hours of thermal power plants decrease, the cost of promoting technology diffusion increases.

Technology diffusion and uneven development

We propose a conceptualisation of the process of technology adoption that takes into account the uneven relative costs of technology implementation, especially country differences in wage levels. The novelties and contributions of our approach are the following. First, we introduce a dynamic macroeconomic model of technology diffusion, which is the first to directly account for the difference in factor cost proportions in an endogenous cross-country setting. Second, we utilise the Cross-country Historical Adoption of Technology (CHAT) and Penn World Table databases to calibrate the model using non-linear approximation across countries and technologies, which explains about 50% of the variability in technology density. Third, the results of the calibrated model offer a new insight into the dynamics and patterns of technology diffusion of differently developed countries, offering both an approximation of the average technology adoption across differently developed countries over time and an approximation of the relative technology density adoption curves, which are country specific generalisations of the logistic curves and depend highly on the general level of development and wage levels.

Energy transition scenarios in the transportation sector in Brazil: Contributions from the electrical mobility

During COP 21, the Paris Declaration on Electro-Mobility and Climate Change & Call to Action defined the global goal of reaching 100 million electric vehicles (EV) and 400 million two- or three-wheel vehicles by 2030. As a result, several countries have set national targets for implementing EV as an integral part of low-carbon energy transition. In Brazil, the technological route of biofuels has stood out from the National Alcohol Program (PROALCOOL) in the 1970s to the current National Biofuels Policy (RenovaBio). However, in recent years, the EV market has been gaining a prominent role in business, governmental and academic debates. Thus, this study seeks to analyse the main factors that drive electric mobility in Brazil, from the definition of technical, economic and fiscal parameters, in addition to evaluating hypotheses on the evolution of public policies and prices of EV in the country. Some scenarios were developed to identify the behaviour of the market penetration of EV in the country, based on Bass' technological diffusion model and dynamic systems. Results indicate that the EV purchase price is consolidated as the most relevant variable for the diffusion of electric mobility in Brazil. From the gains derived from economies of scale, a reduction in the price of vehicles is expected given the decrease in the production costs, as well as in price of batteries. We also concluded that the establishment of a regulatory framework and the development of public policies are fundamental to the promotion of electric mobility in the country.

Digital Divide: An Econometric Study of the Determinants in Information-poor Countries

There can not be two opinions on the importance of Information and Communication Technology (ICT) for economic development. However, real disparities exist in access to and use of ICT across countries. The digital divide is a complicated matter of varying levels of access, basic usage, and applications of ICT among countries and peoples. Using the Gompertz Technology Diffusion model, this paper attempts to measure the contribution of factors such as affordability, knowledge, infrastructure, human capital, trade openness, and economic and social environment in the technology diffusion process, specially in the case of information-poor countries.

Data-driven prediction and evaluation on future impact of energy transition policies in smart regions

To meet widely recognised carbon neutrality targets, over the last decade metropolitan regions around the world have implemented policies to promote the generation and use of sustainable energy. Nevertheless, there is an availability gap in formulating and evaluating these policies in a timely manner, since sustainable energy capacity and generation are dynamically determined by various factors along dimensions based on local economic prosperity and societal green ambitions. We develop a novel data-driven platform to predict and evaluate energy transition policies by applying an artificial neural network and a technology diffusion model. Using Singapore, London, and California as case studies of metropolitan regions at distinctive stages of energy transition, we show that in addition to forecasting renewable energy generation and capacity, the platform is particularly powerful in formulating future policy scenarios. We recommend global application of the proposed methodology to future sustainable energy transition in smart regions.

Diffusion of Agricultural Technology Innovation: Research Progress of Innovation Diffusion in Chinese Agricultural Science and Technology Parks

With the rapid development of agricultural technology in China, a new model of agricultural technology diffusion, represented by agricultural science and technology parks, has been formed. We systematically sort out the progress of agricultural technology diffusion-related research based on a proposed research framework of technology diffusion in agricultural science and technology parks. The growth mechanism of agricultural technology poles, agricultural technology diffusion system, and its characteristics are analyzed. An index system of technology diffusion environment evaluation is summarized. From the perspective of the “basic” paradigm, we discuss the characteristics of the time process (diffusion stage, diffusion speed, diffusion breadth) and the spatial process (diffusion effect, spatial pattern) of technology diffusion in agricultural science and technology parks and their influence mechanisms. The fundamental law of “point-axis” diffusion of technology diffusion in the park is summarized. From the perspective of the “adoption” paradigm, we analyzed the influencing factors and mechanisms of farmers’ technology adoption. The effects of different environments and technologies with different attributes on farmers’ adoption behavior are explored. Based on the latest research results, we summarized new business agents’ technology adoption behaviors and mechanisms. Finally, we point out the issues that need to be further explored in studying the technology diffusion of agricultural innovations.

DeepSolar++: Understanding residential solar adoption trajectories with computer vision and technology diffusion models

•We use computer vision and technology diffusion models to study PV adoption over time •Lower-income communities are delayed in PV adoption onset and saturate more quickly •Performance-based incentives are associated with high PV saturation for low income •Property tax incentives are associated with high PV saturation but not for low income Although the United States has generally experienced a rapid adoption of residential photovoltaics (PV), many communities are lagging behind. To investigate why, we developed a computer vision model that addresses the challenge of low image resolution to identify the installation year of PVs from historical aerial and satellite images. We used the model to construct a granular spatiotemporal dataset of PV deployment across 46 US states and analyzed these data from a technology adoption life cycle perspective. Our analysis of adoption curves and phases showed that low-income communities are not only delayed in their adoption onset but also saturate more quickly at lower levels. We further demonstrated the value of our data via an illustrative analysis of financial incentives and found that performance-based incentives are positively associated with saturated adoption levels—particularly for lower-income communities. Our study highlights the importance of analyzing PV adoption trajectories from dynamic perspectives to inform policy design. Although the United States has generally experienced a rapid adoption of residential photovoltaics (PV), many communities are lagging behind. To investigate why, we developed a computer vision model that addresses the challenge of low image resolution to identify the installation year of PVs from historical aerial and satellite images. We used the model to construct a granular spatiotemporal dataset of PV deployment across 46 US states and analyzed these data from a technology adoption life cycle perspective. Our analysis of adoption curves and phases showed that low-income communities are not only delayed in their adoption onset but also saturate more quickly at lower levels. We further demonstrated the value of our data via an illustrative analysis of financial incentives and found that performance-based incentives are positively associated with saturated adoption levels—particularly for lower-income communities. Our study highlights the importance of analyzing PV adoption trajectories from dynamic perspectives to inform policy design.

Probabilistic feasibility space of scaling up green hydrogen supply

Green hydrogen and derived electrofuels are attractive replacements for fossil fuels in applications where direct electrification is infeasible. While this makes them crucial for climate neutrality, rapidly scaling up supply is critical and challenging. Here we show that even if electrolysis capacity grows as fast as wind and solar power have done, green hydrogen supply will remain scarce in the short term and uncertain in the long term. Despite initial exponential growth, green hydrogen likely (≥75%) supplies <1% of final energy until 2030 in the European Union and 2035 globally. By 2040, a breakthrough to higher shares is more likely, but large uncertainties prevail with an interquartile range of 3.2–11.2% (EU) and 0.7–3.3% (globally). Both short-term scarcity and long-term uncertainty impede investment in hydrogen end uses and infrastructure, reducing green hydrogen’s potential and jeopardizing climate targets. However, historic analogues suggest that emergency-like policy measures could foster substantially higher growth rates, expediting the breakthrough and increasing the likelihood of future hydrogen availability. Green hydrogen is a crucial part of plans to achieve climate targets, yet how quickly supply will scale is unclear. Using a technology diffusion model, Odenweller et al. suggest that even if electrolysis capacity grows as quickly as wind and solar power, green hydrogen supply will suffer from short-term scarcity and long-term uncertainty.

Pathways of clean energy heating electrification programs for reducing carbon emissions in Northwest China

Clean energy heating electrification programs provide a promising way to reduce carbon emissions from fossil fuel combustion and consumption. This work studies the cost competitiveness of clean energy heating technologies under three dynamic mechanisms: investment costs, subsidy policies, and operating costs with real data. It provides key insights into the cost competitiveness of the different heating technologies deployed in different areas, as well as their sensitivity to the three dynamic mechanisms. The results show that currently, the distinct heating programs are more cost-efficient in the urban area with existing heating networks. The average payback period of all district clean energy heating programs in the urban area is 14.9 years, while that of the individual clean heating programs is 24.7 years. The individual heating programs are becoming increasingly cost-competitive with the incentive mechanisms, especially the electricity pricing mechanisms. Moreover, individual heating technologies present remarkable advantages on flexibility and sustainability in the long run. According to the technology diffusion model proposed in this paper, the individual clean heating programs will occupy more than 50% of the market share in 2050 under the comprehensive effect of CAPEX, government subsidies, and OPEX. The real-world results and analysis render references to shape the pathway of clean energy heating electrification in Northwest China and other regions with a similar situation.

Adoption of e-government by Indonesian state universities: An application of Technology Acceptance Model

E-government is the use of internet platforms to deliver governmental services to citizens with the goal of improving government efficiency, transparency, and participation. This study aims to find out what factors determine the rate at which public university in Indonesians accept e-government services. This study uses the Technology Acceptance Model (TAM). TAM is a traditional model of technology diffusion that is an essential conceptual criterion. It drives acceptance consisting of perceived usefulness and perceived ease of use. The results of this paper back up the original TAM hypothesis. Based on the data collected and analyzed, the study concludes that three influencing factors, namely perceived usefulness (PU), perceived ease of use (PEU), and perceived risk (PR), are significantly related to the intention to use e-commerce government. However, trust and social influence do not significantly affect the intention to use e-government services. This paper has significant implications for policymakers and government officials who want to see the growth of e-government usage in Indonesia. When attempting to market a new online system from the perspective of human-computer interaction, it becomes evident that designers must consider how users perceive the risks.

Climate mitigation under S-shaped energy technology diffusion: Leveraging synergies of optimisation and simulation models

Transforming global energy systems is critical for climate change mitigation and requires overcoming not only techno-economic, but also socio-technical hurdles. The main tools to analyse challenges in these two domains are integrated assessment models (IAMs) and transition theories or models, respectively. Despite a surging interest in integrative research that leverages complementarities in order to include social constraints into IAMs, both approaches are often confined to their own disciplinary background and practical integration studies of existing models are scarce. Here I demonstrate the feasibility of model integration by a bi-directional soft-link that merges the strengths of a neoclassical intertemporally optimising IAM with one global region, and a technologically and regionally highly resolved, evolutionary simulation model of S-shaped technology diffusion in the power sector. The new model iteratively converges to a stable equilibrium via two time-dependent coupling variables: carbon prices and renewable energy shares. The results for a 2 °C scenario show that due to gradual technology diffusion, energy transition challenges are exacerbated and incur higher economic losses. I discuss the potential of coupling existing models as an option to combine insights from different disciplinary perspectives to energy transitions.

Asymmetric Evolutionary Game Analysis of Building Information Modeling (BIM) Technology Diffusion

Building Information Modeling (BIM) technology plays a pivotal role in the process of building process informatization. In the context of extensive promotion by all parties, how to effectively evaluate the promotion value of countries and enterprises is particularly important. In order to better analyze the promotion trend of the BIM technology diffusion system in China, a noncooperative evolutionary game model of BIM technology diffusion between the government and enterprises is constructed, and Vensim PLE software is used for simulation analysis in this paper. The model includes government subsidy cost and penalty, social benefit and loss, and income payment matrix of BIM technology input income and cost. The results show that enterprises need not only direct cost subsidy from the government but also indirect cost subsidy in the process of BIM technology diffusion. Appropriate government penalties can effectively promote the initiative of enterprises to adopt BIM technology; BIM has a great impact on the input income and cost of enterprises. In view of this, it is suggested that the first task for enterprises to promote the diffusion of BIM technology is to clarify the definition of BIM's high investment cost and do a good financial impact analysis.

Applying a Model of Technology Diffusion to Quantify the Potential Benefit of Improved Energy Efficiency in Data Centres

Data centres are a key infrastructure for the global digital economy, helping enable the EU “Digital Decade” by 2030. In 2015, data centres were estimated to consume 2.5% of EU electricity demand. In Ireland, the concentrated presence of data centres could consume 37% of national electricity demand by 2028. The uncertainty of data centre facility-level energy efficiency paired with the need to achieve a low-carbon economy pose significant challenge for generation and transmission network planning. This is the first paper to apply a model of technology diffusion with a national forecast of changes in Irish data centre electricity demand through more efficient liquid cooling. The methodology serves as a technology-agnostic resource for practitioners performing forecasts under uncertainty with limited information. Results suggest that technology adoption could lower national electricity demand by 0.81% if adopted by new plant from 2019 to 2028. Savings rise to 3.16% over the same period if adopted by new and existing data centres. Adoption would also lower related emissions by 4.70% and 23.04% over the same period across both scenarios, respectively. Results highlight substantial potential electricity and associated emissions savings available in the sector and suggest policy options to support a transition towards a low-carbon economy.

Study of China's Optimal Concentrated Solar Power Development Path to 2050

As an important form of clean energy generation that provides continuous and stable power generation and is grid-friendly, concentrated solar power (CSP) has been developing rapidly in recent years. It is expected that CSP, together with wind and solar photovoltaic, will constitute a stable, high percentage of renewable energy generation system that will be price-competitive with conventional energy sources. In this study, a dynamic programming approach based on minimum cost was used to explore the optimal development path of CSP generation in China by 2050. A learning curve model and a technology diffusion model were used as constraints. The impact of factors such as Gross Domestic Product (GDP) growth, incentive policies, technological advances, grid absorptive capacity, and emission regulation schemes on the development of CSP generation was discussed in the context of sensitivity analysis and scenario comparison. This study has reached the following conclusions: 1) the government cannot achieve the target for cumulative installed capacity in 2050. Considering the interaction of relevant factors, the target would be hard to achieve even under favorable conditions; 2) as a key factor affecting the development of CSP, the incentive policy is closely related to construction cost. It is noteworthy that although the target can be achieved with a higher investment ratio, the CSP industry has failed to create a good ecological environment in the early stage of development; 3) GDP growth and learning rate are important factors influencing the development path in later stages; and 4) although they operate as potential factors affecting construction costs, grid absorptive capacity and carbon permit prices have limited impact on the development of CSP generation.

Technological change or process innovation – An empirical study of implemented energy efficiency measures from a Swedish industrial voluntary agreements program

The implementation of energy efficiency measures (EEMs) is the primary means of improving industrial energy efficiency (IEE). Almost 69% of industrial electricity use emanates from motor systems. IEE has traditionally been explained with technology-diffusion models. According to these models, increased diffusion of more effective motors and drives onto the market leads to improved efficiency. Electric motor systems are represented by three levels: electric motor, core motor system, and total motor system. The first and second levels are related to stand-alone technology measures while the third has a more operational character. Based on a unique dataset of implemented energy-efficiency motor system measures from the 100 most electricity-intensive industrial companies in Sweden, the aim of this paper is to study whether technology diffusion is a valid model for understanding improved energy efficiency in electricity-intensive industries. Results show that 59% of the implemented EEMs from the dataset go beyond technology diffusion models, implying a need for revised models to understand how energy efficiency and technology diffusion occur in energy-intensive industry.