Jianhua Zhang (p), Dimitris Ballas, Xiaolong Liu

Martin Faulques

There is a rapidly growing number of studies on transnational renewable energy technology diffusion. Most of these studies have generally adopted a bilateral perspective, with countries considered as agents in the diffusion process. However, renewable energy technology diffusion is often the result of interactions among firms and involves strong network effects. In this paper, we explore the global renewable energy technology diffusion from a network perspective, with multinational corporations (MNCs) as network makers. In particular, we first propose a methodology to construct the global renewable energy technology diffusion networks relying on patent data related to climate change mitigation technologies (CCMTs), intra-firm relationships, and business scales of the selected MNCs. The network capital for each country is calculated, which is eventually used as input for the econometric analysis to investigate the network effects on renewable energy technology development. The network statistical analysis reveals an uneven geography of network capital, indicating global disparities in renewable energy technology development. Moreover, the econometric analysis identifies strong network effects derived from linkage volumes and structural positionalities within the renewable energy technology diffusion networks.

Thiago Pastorelli Rodrigues (p), Paula Pereda

Jianhua Zhang

Renewable energies have become central for global sustainable development, and Brazil has great potential for exploring solar sources. Brazilian states have implemented a tax incentive to push for small-scale renewable energy market development. Based on the net metering mechanism, the states exempt the distributed generation systems owners from electricity tax equivalent to the amount of electricity exported to the distribution grid. In this paper, we aim to estimate the effect of this policy on solar photovoltaic (PV) adoption. The literature on government incentives to promote renewables is largely based on policies applied in developed countries. To the extent of our knowledge, this study will be the first to assess the effect of electricity taxes exemption on the adoption of small-scale solar photovoltaic systems. We build a monthly municipal-level panel combining a novel administrative data set of distributed generation systems with socio-economic information from 2014 to 2019. We then use the policy staggered adoption by states from April 2015 to June 2018 and the recent developments of the differences-in-differences literature to assess the causal effects of the policy. The results suggest a positive impact of the state tax incentives on solar PV adoption. The policy has created 14% of the installation after treatment, which translates into 8 GWh energy savings in five years.

Boshuai Qiao (p), Sigal Kaplan, Jie He

Thiago Pastorelli Rodrigues

Predicting the future market share of new energy vehicles is important for analyzing transport externalities, and optimizing infrastructure deployment. We estimate deep learning models for representing the market demand under dynamic market conditions. We use Artificial Neural Network Multi-Layer Perceptron (ANN-MLP) and Bidirectional Long-Short-Term-Memory model (BiLSTM) for analyzing the market dynamics between 2015-2022 in Jiangsu, China. Then, we formulate maximum, average, and minimum growth scenarios, and we use the model for predicting the BEV market in 2028. The model predictions are compared with the Gompertz model. We use publically available data about vehicle sales, price changes, fuel-to-electricity ratio, charging piles, driving range, and green license registration. The results show the advantage of the deep-learning models in generating realistic predictions based on market dynamics and covariates. The models present a good fit to historical data and encompass the complexity of the diffusion phenomenon, resulting in a realistic depiction of the diffusion progression and realistic future predictions.

Martin Faulques (p)

Boshuai Qiao

Questions about the location factors of biogas units have become increasingly important in the scientific literature in recent years (Ferrari et al., 2022); certain characteristics seem to be essential for the emergence of biogas units, such as the methanogenic potential or the characteristics of the operators. Due to structural differences between countries (Schumacher and Schultmann, 2017), the installation criteria may vary from one area to another. We will look at rural biogas units in the French Grand-Ouest over a period from 1990 to 2020. The aim of this paper is to assess which criteria can explain why biogas units are located in certain areas and not others. To do so, we use a geographically weighted regression model (Li et al., 2022) to identify the installation criteria of biogas units, using a database containing numerous explanatory variables. After identifying the common characteristics of the installations, it will be possible to identify potential areas where new biogas units could emerge.


The second objective of the article is to study the impact that the diffusion of a new technology (here the biogas) can have on its development (Morill et al, 1988; Hägerstrand, 1967). The literature on this topic suggests that the emergence of a new technology - innovation - in an area can have positive effects on individuals living in the vicinity of the adoption of this technology (Bollinger and Gillingham, 2012). Moreover, the location of biogas units over time depends not only on the anaerobic potential, but also on the spatial dispersion of the precursor entrepreneurs. Subsequently, the imitating entrepreneurs will homogenize the presence of units in space. The rate of establishment of new units in a given area is limited by the anaerobic potential of the area in question, but also by the availability of precursor entrepreneurs and then imitator entrepreneurs. We therefore aim to test the entrepreneurial culture in agricultural areas, which can be spatially different. By integrating the results of the first part, we compare and measure how a biogas unit can affect the emergence of new units on a territory. These effects of the spatial diffusion of biogas units can help us to better understand the mechanisms for the establishment of biogas units, and enable public policies to target territories for the deployment of new biogas projects.