ºÚÁÏÍø´óʼÇ

Katja Ignatieva (ºÚÁÏÍø´óÊÂ¼Ç Sydney)

Kazuhiko Ohashi (Hitotsubashi University, Japan)
This study employs quantile regression to examine the impact of solar photovoltaic (PV) power generation on both the level and variability of wholesale electricity prices. The analysis is based on data from April 2016 to March 2020 for the Kyushu region of Japan, which is particularly suitable for this study given its high solar PV penetration, limited interconnection capacity with other regions, and distinct seasonal variations. Results confirm the merit-order effect and demonstrate a novel finding of seasonal variation in the impact of solar PV power generation on electricity price variability: increased solar PV power generation is associated with reduced price variability in spring and summer, but not in autumn and winter. This seasonal divergence is attributable to changes in the relationship between electricity demand and solar PV output, driven by temperature-dependent demand and positive correlations between temperature, solar radiation, and PV generation. The findings have broader implications for electricity markets with high solar PV penetration and subject to seasonal changes. For policymakers and electricity market participants aiming to mitigate price fluctuations, managing PV-induced variability is more critical during low-temperature (than high-temperature) seasons. Moreover, the valuation of real options for solar PV-based storage facilities may differ between low- and high-temperature periods. A nuanced understanding of seasonal supply-demand dynamics is essential for accurately assessing price risks, evaluating the value of solar PV investments, and formulating effective policies for renewable energy integration.

Marcel Prokopczuk (Leibniz University Hannover, Germany)
In this paper, we study the emergence and elimination of arbitrage opportunities in computerized limit order markets. We measure price changes, buy/sell market/limit order submissions/cancellations at message frequency during arbitrage opportunities in NYMEX WTI futures and their options and find that market makers update quotes before trades become profitable so that actual arbitrage trades hardly ever occur. In the more liquid futures market, executed trades eliminate arbitrage, whereas in the options markets, liquidity providers are the ones who correct arbitrage opportunities. These results highlight the critical role of liquidity providers in maintaining price efficiency.

Stefan Trueck (Macquarie University Sydney)
While corporate social reporting (CSR) and voluntary carbon disclosure become more mainstream, research in the realm of discretionary carbon reporting mostly focusses on its determinants, whereas there is little-known knowledge on its informational value and its effects. Applying theoretical lenses from the management and legitimacy view of environmental disclosure as well as insights from impression management, we revisit the relation between carbon disclosure and performance, extending prior studies by considering disclosure quality. Based on a global sample of CDP participants from 2010 to 2019, we examine the impact of carbon disclosure and its quality on subsequent changes in carbon performance, while accounting for the hard-to-quantify nature of CSR reports by utilizing computer-based linguistic analysis to detect opportunistic reporting behavior. Evidencing that better carbon disclosure performance does not invoke reductions in carbon emission intensity, we argue that disclosure is driven by legitimacy reasons. We further find that increased opportunistic reporting behavior weakly indicates worse future performance, even more pronounced if companies are subject to less stakeholder pressure and regulations.

Desiree Lucchese (Impact Alpha Partners | Responsible Investment Association Australasia)
This presentation questions the usefulness of formalistic modelling approaches often used in climate and energy transition analysis. Despite their technical sophistication, users of these models may fail to capture real-world dynamics and fail to adequately inform investment decision-making.

Models are not inherently flawed. They are frameworks for telling stories about possible futures, shaped by the values, assumptions, and emotions of those who build them. Interpretation is not about uncovering a single truth, but about making sense of uncertainty to serve human understanding. Reliable models, based on robust and credible disclosures and an understanding of their sub-models, can bridge information gaps, helping investors and the public make better-informed choices. Yet recent analyses show how many climate scenarios are misused and can obscure material risks – leaving investors and stakeholders blind to what really matters.

As investors and institutions increasingly rely on a diversity of models to track climate transition risks and opportunities, caution is essential. This presentation calls for a shift from abstract modelling toward transparent, context-aware, and purpose-driven approaches that genuinely illuminate the pathways of climate and energy transformation and reduce greenwashing risk – rather than obscure them behind a façade of quantitative precision. In order to yield genuine insight or practical value, models need to reflect scientific evidence, actual market dynamics and be disclosed with clarity and quality statements.

Vitali Alexeev (UTS, Sydney)
Energy and commodity firms increasingly publicise their sustainability credentials, yet many claims remain difficult to verify, blending environmental ambition with selective disclosure. This presentation illustrates how modern NLP and generative AI techniques (particularly retrieval-based systems) can analyse corporate reports, voluntary carbon disclosures, and media narratives to identify potential greenwashing. Drawing on examples from carbon trading and electricity markets, the talk highlights how these tools enhance transparency, evaluate disclosure integrity, and ultimately strengthen market accountability.