Poster Installation

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This research analyzes the economic feasibility of electric two-wheelers (e2W) n Kenya's informal transportation sector, specifically focusing on Boda-Bodas. Current studies often utilize a low social interest rate of 10%, following the central bank's rates; however, this neglects real-world credit constraints, as riders frequently face interest rates as high as 40%. This research adjusts the discount rate to 40% to incorporate the riders' actual opportunity costs. Furthermore, it incorporates insurance and fees proportional to the asset price into the upfront costs to reflect the true financial burden on riders. After applying Kenya's new mobility tax benefits released in February 2026, we confirm that e2W is significantly beneficial to riders even under high interest rates, provided that initial upfront costs are reduced through policy support. Even under a conservative scenario that excludes learning effects, the reduction in operating expenditure offsets the financial burden on riders. These results suggest the critical importance of fiscal policy and micro-financial structures for the energy transition in developing countries.

The increasing integration of variable renewable energy (VRE) sources into modern power systems presents significant challenges for system stability and operation due to their intermittent and stochastic nature. Power system flexibility has become an essential attribute to accommodate renewable generation variability, enabling the system to respond efficiently to fluctuations in supply and demand. However, quantifying the economic value of this flexibility requires robust conceptual frameworks that facilitate investment decision-making and market design. This study addresses the question: How can the value of flexibility in power systems with high VRE penetration be defined and quantified?. The methodology consisted of a literature review on definitions and operationalizations of flexibility value, analyzing multiple existing conceptual frameworks, including cost and value perspectives, immediacy value, ramping capability, cost savings, and realized power prices, and examining the applicability of Real Options theory as a unifying framework. The findings reveal that there is no universally accepted definition of flexibility value, with approaches ranging from market-value factors to system-cost-savings metrics. Real Options theory is a promising framework that extends financial valuation principles to physical assets and operational decisions, capturing both operational flexibility (dispatchable generation, storage, and demand response) and strategic flexibility (deferral, expansion, or abandonment of projects). The implications of this study point to a critical gap requiring the modeling tools capable of capturing complex interdependencies in renewable-based systems. While Real Options theory offers a powerful analytical lens for energy system decarbonization, its implementation requires stakeholder consensus about the underlying flexibility asset. Alternative frameworks-including cost perspectives, immediacy value, ramping capability, and cost savings-remain equally valid depending on market structures. Successful flexibility valuation ultimately depends on achieving multi-stakeholder consensus on valuation methodologies, clear identification of underlying flexibility assets and transparent mechanisms that fairly distribute costs and benefits across the energy system.

Africa's vulnerability to the impacts of climate change, endemic poverty, poor institutional capacity, ecosystem degradation and limited access to capital mean that most of the continent has weak adaptive capacity. In a rapidly evolving and complex world, achieving several SDGs requires more than talent and resources, it demands effective leadership and the development of robust capacities. African countries urgently need to build endogenous capacities across a wide range of fields relating to the development and deployment of renewable energy that suit the current sustainable energy transition agenda. Renewable energy opportunities in Africa have not been much exploited because of deficits in human, organizational and institutional capacity. A systematic literature review was used to identify and discuss leadership capabilities in terms of styles and skills that can enhance capacity building in favor of energy transition. The findings indicated a range of both soft and hard skills necessary for achieving capacity building under the different energy projects. Such skills included but were not limited to adaptability, vision, inspirational, innovation, risk taking, time management, communication, motivation among others. On the other hand, the leadership styles that were emphasized along included transformational and transactional styles in the management of some unique renewable energy projects. This study then recommends that capacity building should be addressed and implemented as a vital ingredient and blended with a range of leadership skills like adaptability , risk management. This is to be hand in hand with suitable leadership styles like transformational all aimed at realizing a sustainable and affordable energy transition agenda. 

This study presents a transparent, moderately complex modelling framework to evaluate the marginal cost of meeting incremental peak demand, including the operational implications of renewable-only data centre loads. The framework is designed to bridge the gap between overly simplified policy-oriented models, such as levelised cost approaches, and highly detailed capacity-expansion planning tools. Its key contribution is the integration of stochastic interannual variability in renewable generation, a novel representation of green load and demand flexibility, frequency control ancillary services, and renewable drought impacts within a tractable system-level model.
The stochastic linear programming formulation optimises the generation and storage capacity mix required to meet an incremental gigawatt of peak demand, including data centre loads, across dominant technology and fuel options. Particular emphasis is placed on representing carbon-neutral data centre demand and its coupling with renewable and storage resources, while explicitly capturing the value of demand flexibility for large, schedulable loads.
The model is applied through case studies in Southern Queensland (Australia), Northern Hebei Province (China), and India to illustrate how optimal capacity mixes and system costs respond to green load requirements and emissions constraints. Results indicate that the investment cost of meeting incremental peak demand varies substantially, from approximately $1,025/kW in China to $1,615/kW in India and up to $3,045/kW in Australia. Lower capital and operating costs of coal generation in China and India result in coal dominating the capacity mix, accounting for 84% and 71% of peak capacity respectively.
While results are illustrative due to the model's simplified dispatch structure, the framework provides a robust and easily implementable tool for testing decarbonisation pathways and identifying conditions under which renewables, storage, firm thermal generation, and clean baseload options such as nuclear can contribute to least-cost system outcomes.

With the increasing integration of renewable energy into the grid, the marginal costs of electricity generation have shown significant temporal variation. However, retail electricity tariffs remain predominantly flat-rate, creating a structural misalignment between generation costs and retail prices. When retail prices deviate from time-varying marginal costs, the price system fails to provide efficient signals, resulting in allocative inefficiency and welfare losses.This study quantitatively evaluates the economic losses arising from flat-rate pricing using granular bidding data from a wholesale electricity market. It also investigates how alternative pricing schemes, such as time-of-use tariffs, could reduce allocative inefficiency and provides implications for more efficient electricity pricing design.The analysis employs transaction data from the Japan Electric Power Exchange (JEPX), The national wholesale electricity market. The dataset includes bid prices and quantities submitted by buyers and sellers, as well as market-clearing prices at 30-minute intervals from 2023 to 2025, yielding approximately 50,000 observations. Based on these bids, stepwise demand and supply curves are reconstructed for each market. This approach allows us to compute the deadweight loss (DWL) from fixing electricity prices away from equilibrium price, without imposing functional forms on demand and supply.The study then computes the price that minimizes annual DWL and compares it with prevailing flat-rate pricing schemes. The results indicate that annual DWL in 2023 is approximately 40.2 billion Japanese yen, corresponding to about 1.4% of total market transactions in that year. Theoretically, this economic loss would be eliminated under market-linked pricing reflecting temporal variation in marginal costs. These findings demonstrate that flat-rate retail tariffs have significant implications for economic efficiency and provide quantitative evidence to inform cost-reflective electricity pricing systems.

Climate change commitments, net-zero targets, and regulatory pressures are accelerating corporate transitions toward sustainable business models. Firms are utilising several strategies including internal innovation and mergers and acquisitions (M&As) to augment the sustainability portfolio of their organizations (Flammer, 2021; Krüger, 2015). Markets increasingly evaluate such sustainability strategies as indicators of competitiveness and regulatory preparedness (Flammer, 2021; Krüger, 2015). However, firms differ in their pre-existing green innovation capabilities prior to the M&A deal. This variation can influence the anticipated strategic output from the acquisition and from the investors. This raises an important question regarding how markets respond when environmentally capable firms get involved in green versus non-green acquisitions, and whether green deals generate different market responses compared to non-green deals within the same firms.
The present study addresses this gap by focusing on publicly listed Indian firms possessing green patents and examines investor responses to green versus non-green merger and acquisition announcements between 2010 and 2024. Green deals are identified through text and content analysis done by researchers and also supported by Natural Language Processing (NLP), and market reactions are estimated using an event study approach based on the Fama–French asset pricing framework.
Results suggest that green acquisitions generate more positive abnormal returns relative to non-green acquisitions undertaken by the same firms, indicating that investors reward strategic alignment between internal environmental capabilities and external investments. The results further suggest that firms with stronger environmental innovation intensity experience greater valuation gains. 
These findings offer practical implications to managers, investors and industry stakeholders by highlighting how green acquisitions enhance market valuation when they reinforce existing environmental capabilities. While governments learn how capital markets encourage businesses to promote low-carbon transition pathways, they should use strategically consistent green investments to boost investor confidence.

In sub-Saharan Africa, women bear a disproportionate burden of unpaid care work. Energy poverty-affecting 600 million people in the region (IEA, 2024)-directly produces time poverty, as women must collect biomass for hours daily. RETs like solar cookstoves and pumps could alleviate this by reducing time spent on domestic tasks, improving health, and lowering fuel costs. However, adoption remains low.
This study examines the systemic barriers hindering the adoption of renewable energy technologies (RETs), such as solar irrigation systems or improved cooked stove, by women micro-entrepreneurs in Senegal, while analyzing the roles of suppliers and microfinance institutions (MFIs) within this value chain. The research is guided by the following question: what are the socio-economic, financial, and structural barriers preventing women entrepreneurs from adopting RETs, and how can these be addressed to foster economic empowerment and climate resilience?
This qualitative study draws on 87 semi-structured interviews in Senegal's Niayes region with 78 women entrepreneurs, six RET suppliers, and three microfinance institutions (MFIs). Data were coded and analyzed using NVivo software.
Findings reveal that women's RET adoption is constrained not only by high upfront costs and limited finance, but also by entrenched gender norms that allocate domestic responsibilities to women and position men as household decision-makers. Suppliers face financing and logistical challenges, while MFIs lack tailored financial products for RETs.
This research contributes to the literature on social and climate justice by analyzing the just energy transition by integrating gender and energy poverty. By adopting a systemic value chain perspective, the study illuminates how intersecting barriers-spanning households, suppliers, and financial institutions-shape the energy transition for women entrepreneurs. Addressing gender-blindness and accessibility in energy value chains, this study centers women's voices to reveal how intersecting vulnerabilities shape their participation in the green economy offering critical insights for an inclusive transition.

Low-carbon technologies are central to achieving global climate goals, yet what matter are not only how fast the innovations emerge but also how widely they diffuse across regions. Past decades have witnessed striking developments of low-carbon technologies, but the majority of which were concentrated in developed countries and a few emerging economies like China, while most developing regions have limited access to advanced technologies. This could decelerate global low-carbon transitions because lagged regions may face higher abatement costs, and stronger incentives for carbon-intensive production to persist, if new technologies cannot be efficiently shared across regions. Meanwhile, climate policies (e.g., carbon pricing) and trade policies (e.g., tariffs and carbon border adjustment) can jointly shape both the pace of low-carbon innovation and the channels through which technologies spread internationally. 
In this context, we investigate into how low-carbon technology could be diffused with trade, and how could climate and trade policies affect this process. To answer these questions, we develop a tailored computable general equilibrium model that (i) captures baseline cross-country differences in low-carbon technologies and policy-induced technical changes, and (ii) embeds an explicit inter-regional diffusion mechanism linked with bilateral trade in intermediate inputs and capital goods, so that trade relationships become a concrete vehicle for technology transfer. A key contribution is calibrating regional- and sector-specific innovation capacity parameters using patent evidence from PATSTAT, reflecting heterogeneous invention and spillover potential. 
We simulate the model dynamically from 2017 to 2035, considering various policy scenarios. Results show that enabling technology diffusions reduces global welfare costs by more than 20% than no-diffusion baseline. Moreover, a universal higher trade barrier weakens diffusion and offsets roughly half of these gains. This implies the significance of considering trade-induced technology diffusion in policy evaluations. 

Residential energy rating schemes are increasingly expected to inform not only building efficiency standards but also household investment decisions, distribution network planning, and broader decarbonisation pathways. This paper addresses the research question: how can residential energy ratings be enhanced to accurately reflect time-resolved building performance, inform distributed energy resource (DER) investment, and guide economically efficient network operations?


We first review Australia's Nationwide House Energy Rating Scheme (NatHERS) within the international landscape of residential energy rating systems, identifying methodological gaps that limit policy and system relevance. Key gaps include limited treatment of demand flexibility, incomplete integration of rooftop photovoltaics (PV) and battery energy storage systems (BESS), and a validation gap between asset-based scores and real operational outcomes. We highlight the potential for ratings to support economically informed investment decisions by homeowners, policymakers, and network operators, including peak-demand management, hosting-capacity assessment, and DER valuation.


To address these gaps, we develop a "whole-of-home" optimisation framework that couples hourly thermal and electrical load modelling with household-level PV–BESS capacity and dispatch decisions. The framework explicitly incorporates tariffs, export limits, and feeder-level network constraints, enabling co-optimisation of investment and operations while capturing network impacts and economic signals. Application to representative Australian suburban feeders demonstrates heterogeneity in optimal DER sizing, the role of network constraints in shaping DER value, and the potential for coordinated investments in solar, storage, and demand flexibility to reduce or defer costly network augmentation.


By linking time-resolved building-level decisions to network and market outcomes, the approach provides actionable insights for economically efficient DER deployment and network planning. The framework advances residential energy rating schemes toward system-aware, investment-relevant assessment, aligning household incentives with tariff structures, network efficiency, flexibility, and decarbonisation objectives.

As electric vehicle adoption accelerates, the need for fast charging infrastructure has become critical to reducing range anxiety, a major barrier for potential users. Ultra-fast charging stations, delivering power rates above 150 kW, are essential to provide a convenience level comparable to traditional fuel stations. However, their deployment faces challenges. Grid capacity is a key constraint, as electrical networks in many countries are already saturated. In addition, the profitability of fast charging stations remains uncertain due to high upfront investment costs and variable utilization rates.
Given these constraints, identifying an effective business model for fast charging stations with limited grid connection is essential. This paper investigates the optimal posted pricing strategy when a guaranteed power rate is offered to users. It derives the optimal price, power-rate menu, and number of charging points under a no-queue model, using information on the distribution of user charging preferences. The analysis also characterizes the benefits of integrating a battery into a fast charging station.
Several setups are explored. The initial setup assumes constant energy costs and a steady EV arrival rate. In this case, the optimal strategy for the charging point operator (CPO) is to offer a power rate equal to the maximum EV charging capability, with just enough charging points to fully utilize grid capacity. The optimal price is higher than in the absence of grid limitations. The second setup introduces peak and off-peak arrival periods and examines the use of a battery to smooth demand. The paper further analyzes whether battery use is more valuable for serving additional EVs or reducing energy costs, and studies the impact of peak pricing charges on market equilibrium.
Overall, the paper provides a comprehensive framework for evaluating the profitability of fast charging stations, highlighting the roles of grid constraints, battery storage, and peak pricing.

Carbon capture, utilization and storage (CCUS) is widely recognized as a critical technology for achieving deep decarbonization in fossil-based energy systems and hard-to-abate industrial sectors, playing an indispensable role in global climate mitigation.
However, large-scale deployment of CCUS in China faces substantial cost challenges, particularly in the transport segment, due to the spatial mismatch between emission sources and inland storage sinks. The temporal overlap between declining fossil fuel demand and accelerating CCUS deployment presents a strategic opportunity to repurpose existing oil and gas pipelines for CO2 transport, reduce CCUS deployment costs and accelerate its development.
To explore how existing oil and gas pipeline infrastructure can be leveraged to construct an efficient CO2 transport network, this study develops a nationwide source–sink matching and pipeline optimization model covering four major sectors in China, including power generation (coal-fired, oil- or gas-fired, and biomass power), coal chemical, iron and steel, and cement. By integrating existing oil and gas pipelines with newly constructed CO2 pipelines, a nationwide shared transport network connecting multi-sector emission sources with both onshore and offshore storage sites is established.
The results indicate that 931 potential emission sources across multi-sectors in China provide approximately 8.17 Gt of CO2 mitigation potential through CCUS. The repurposing of long-distance pipelines can significantly reduce transportation costs and abatement costs, greatly improve the economic accessibility of inland storage, and thus reshape the competitive dynamics between inland and offshore storage schemes.
These findings suggest that incorporating existing fossil fuel transport infrastructure into comprehensive CCUS network planning can not only simultaneously mitigate the risk of stranded asset, but also reduce CCUS costs and accelerate its large-scale development nationwide, offering important policy implications for infrastructure transition and climate policy design.