THE UNIVERSITY OF HONG KONG
DASE7099 Dissertation
Research Proposal
LLM-enabled financial impact quantification considering climate condition
for logistics industry
Submitted by
[ZHANG, Siyuan]
Student No.: [3036804830]
Supervisor: Ray Y. ZHONG
October 2026
1
Abstract 所研究的问题、主要研究问题、目标、方法和预期结果 300
Logistics operations are exposed to climate conditions such as typhoons, extreme heat,
heavy rainfall and sea-level rise. A closed port, a diverted route or damaged cargo can change
revenue, operating cost, inventory and asset values. Research on ports and supply chains has
measured downtime, trade exposure and adaptation cost. Research in finance has measured
effects on profit, financing and the value of physical assets. A thinner step remains between
these results: a path, for one logistics setting, from a climate condition to financial figures that
can be checked in a written statement. Large language models can read records and draft that
statement. The figures themselves have to come from a calculation, because a fluent sentence
is not evidence of an amount.
This proposal sets out an MSc study of LLM-enabled financial impact quantification
under climate conditions in the logistics industry. The study will investigate how a climate
condition passes into logistics operations and then into financial impacts. It will establish a
calculation for revenue loss, additional operating cost, inventory loss, and asset repair or
impairment. A large language model will be used after the calculation, to draft disclosure
wording in which each figure refers to a row in the table. The case data will be provided by
the supervisor. The calculation rules and the checking procedure will be fixed before that file
arrives. The expected results are the calculation table, a traceable draft, and a check of
whether the draft keeps the calculated figures. For this dissertation, the contribution is a
workable pathway for one logistics setting, not a new theory of climate or finance.
Section 1 Introduction
1.1 Background of study 500-600 568
Nowadays, extreme climates around the world are becoming increasingly prominent. El
Nino and La Nina phenomena occur even frequently. They bring loads of abnormal climate
2
conditions, such as high temperature, typhoons and unstable precipitation. Unstable climatic
conditions have had a huge impact on the logistics system. Port is a hub connecting sea
transportation and inland distribution. It is quite important but is easily influenced by wind,
flood, extreme heat and sea level rise. (Becker et al., 2018; Izaguirre et al., 2021) The chaos in
ports not only affects the terminals themselves, but also the trade flows and the logistics
services that rely on these flows. (Verschuur et al., 2023a, 2023b) For instance, typhoons
change port operations. And the port closures related to typhoons further cause economic
losses in coastal areas. (Zhang et al., 2020; Tian et al., 2023) Therefore, for logistics
companies, climatic conditions are both an environmental event and an operational event.
In existing research, climate risk always been regarded as a threat factor to the supply
chain. And extreme weather caused more severe losses to logistics. (Ghadge et al., 2020; Er et
al., 2021; Malik et al., 2022) However, studies on weather conditions in transportation still
find a gap between describing the hazards and responding specifically to them. (Touloumidis
et al., 2025) Researches on port calculate what the adaptation cost is and when investment
should be made. (Yang et al., 2018;Wang & Zhang, 2018; Randrianarisoa & Zhang, 2019;
Leon-Mateos et al., 2021) However, the company's specific revenue, cost, inventory and asset
data have not formed a concrete and quantified connection with the climate. Therefore, it is
difficult for enterprises to make corresponding and accurate responses based on specific
meteorological events. Therefore, sudden meteorological disasters still bring significant
operational impacts to logistics enterprises. These business shocks have financial weights and
typically include profits, financing terms or asset values. (Pankratz et al., 2023; Huang et al.,
2018; Kling et al., 2021;Ginglinger & Moreau, 2023; Huang et al., 2022) Investors in logistics
enterprises demand more direct disclosure of economic impacts. (Krueger et al., 2020;
Flammer et al., 2021; Ilhan et al., 2023) Research on asset level warns that if quantitative
analysis is ignored, storm losses will be magnified. (Bressan et al., 2024; Mandel et al., 2025)
3
Xiong et al. (2025) studied on logistics financial condition. But what they measure was the
reaction of the stock market. The quantitative calculation of dividing a logistics event into
revenue, cost, inventory and assets was still lacking.
The application of LLM can solve the key problems of this issue. Because the records
about this issue are mixed. Some fields are numbers, while others appear in text form in
reports, access records and disclosure paragraphs. (Zhao et al., 2026; Huang et al., 2023)
Generative models are regarded as a supply chain capability for perception, analysis and
communication. (Jackson et al., 2024; Fosso Wamba et al., 2023, 2024) Therefore, in this
issue, LLM can be used to integrate all the data of climate factors and economic impacts.
Leippold (2023) established a smooth climate finance model that was not based on records.
This limitation determined the design of solution to the problem. The model established with
the help of LLM assists in reading abundant weather and financial texts and drafting
explanations. This helps enterprises to take corresponding and efficient measures when facing
different weather conditions, thereby reducing the impact of extreme weather on the logistics
industry.
1.2 Description of the studied problem 598
Because of global warming, extreme weather conditions are occurring more frequently.
These sudden meteorological events caused great losses to the logistics industry. However, at
present, the logistics industry in China lacks a traceable path for the financial impact of
climatic conditions. This requires the indicator decomposition of meteorological conditions
and the financial situation. And then through big data analysis, determine the quantitative
relationship between them.
This study quantitatively investigates the impact of ports and supply chains on
downtime, trade risks, and adaptation costs. It breaks down financial impacts into multiple
4
specific indicators, such as quantitative performance, financing and asset value. Besides, this
article studies meteorological events that are relatively common in China under climatic
conditions, including typhoons, heavy precipitation, and high temperatures. The final selected
indicators will be adjusted based on the data obtained later. LLM will be used to study reading
texts or drafting texts. This article will connect these steps to a setting and keep the
connection clear.
The goal is:
1. Investigate how climate conditions affect logistics operations and financial statement
items.
2. Establish a quantitative approach for the financial impact from climatic conditions to
the logistics environment.
3. Develop a program to convert the quantified results into disclosure reports and
evaluate whether these reports retain the calculated data.
The research questions are divided into four. The first one is which climate conditions to
use. Port research has documented acute events, especially typhoons and related wind or flood
damage. These are most likely to be recorded in the operation records. (Zhang et al., 2020;
Tian et al., 2023) The selection will follow the data provided by the mentor later on. The
event with the most complete data will become the main research case. The second issue is
the impact on the operation channels. Which facilities, routes, goods or inventories were
disrupted and for how long all need to be analyzed. The third is the financial channel. This
study calculates four parts, including revenue loss, additional operating costs, inventory loss,
and asset repair or impairment. The last one is the application of LLM in program
development. It can extract event facts from the text that provides data and draft the wording.
(Leippold, 2023; Fan et al., 2025)
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The research task is to solve four research questions. Before the data be gained, the
variable table, calculation rules, disclosure list and prompts will be prepared in advance. After
receiving the data, they will be classified and organized into climate events and financial data
variables related to financial amounts. This article will calculate the baseline scenario and the
climate event scenario, and conduct sensitivity checks on the number of interruption days and
any loss rates that cannot be directly observed. Then it will provide the model with any
narrative records contained in the table and data.
As for the range, this study utilized the logistics data of a specific enterprise. It takes this
enterprise as an example to establish a model that can be widely applied to other logistics
enterprises. The skills that still need to be learned are the understanding of various data in
financial projects, written assumptions, and the methods used by LLMS in this study. These
tasks will be mastered within a month and then be applied to data processing and modeling.
The expected outcome is first to establish a quantitative relationship model framework
between climatic conditions and financial impacts in the logistics industry through a specific
case. The second is a documented calculation table and a brief record of traceability and
coverage. As a result, a decision support path for a logistics case was obtained.
1.3 Outline of research methodology
This study combines a clear computational model with a separate case. By analyzing the
data of a specific logistics enterprise, a model of the relationship between climatic conditions
and financial data is established. The specific research steps are:
1. Read a large number of documents. Understand and organize relevant research on
climate conditions, financial data and LLMS. Identify the gaps and variables in this research
field.
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2. This study adopts the LLM research method. It adopts a transmission framework, four
computing rules, and is in charge of data processing and language model validation. This
study uses LLM to establish a model to analyze the obtained enterprise data, thereby
obtaining the quantitative relationship between climatic conditions and financial impacts.
3. Through the established model, further develop a program that can convert quantified
results into disclosure reports. The program can also achieve data evaluation and analysis.
Section 2 Literature review 主题、特点和方法以及正在审查的研究工作的
主要贡献 949
2.1 Large language models
The work of artificial intelligence in supply chain management began with prediction,
classification and optimization. (Toorajipour et al., 2021) Later, the combined application of
machine learning further enhanced the functionality of LLMS, especially in terms of text
reading ability. (Vlachos & Reddy, 2025) Zhao et al. (2026) summarized the role of these
models in long documents, including namely reading, compressing and generating. In the
field of finance, FinBERT demonstrated that language models could be trained to extract
information from financial texts rather than merely from general articles. (Huang et al., 2023)
Lee et al. (2025) reviewed the later finance-specific models that continued this route and
further improved them.
In the supply chain, Jackson et al. (2024) viewed generative models as capabilities for
perception, analysis, and communication. Fosso Wamba et al. (2023, 2024) raised the benefits
of drafting and concerns about accuracy. Srivastava et al. (2024) used maps in risk and
documentation. AlMahri et al. (2026) linked the model output to the knowledge graph,
endowing the model with the ability to systematically organize information. Al-khatib et Al.
7
(2024) examined the performance of digital supply chains. Fan et al. (2025) went a step
further by measuring supply chain risks at the enterprise level from the text.
The same literature illustrated the limitations of the application of this suggestion.
Leippold (2023) demonstrated that large language models could discuss climate financing, but
the answers were unfounded. In terms of financial impact, this distinction was very important.
The model in this study consists of one reader and one drafter. The calculation table remains
the source of the amount.
2.2 Financial impact quantification
Investors regarded climate risks as financial issues and demanded disclosure. (Krueger et
al., 2020; Flammer et al., 2021; Ilhan et al., 2023) These articles demonstrate that written
statements of financial impact were reasonable. However, they did not explain how logistics
companies should calculate the impact on financial data. Quantitative evidence was most
evident at the performance and financing levels. Huang et al. (2018) established the
connection between climate risk and corporate performance and financing choices in various
countries. Kling et al. (2021) found that climate vulnerability increases the cost of funds and
tightens financing channels. Linglinger and Moreau (2023) found that in the period after
climate information became more standardized, the leverage ratio was lower in areas with
higher physical climate risks. Huang et al. (2022) found stricter bank loan terms. They also
found that risk management practices could mitigate this impact. Pankratz et al. (2023)
provided the most direct bridge for climatic conditions. They proved that more extreme
calories were associated with lower income and operating income.
In addition to the average revenue of the company, there were also many studies on
physical assets in the literature. Bressan et al. (2024) demonstrated that asset location and
acute disasters, including hurricanes, had largely altered the projected losses. Mandel et al.
8
(2025) mapped the financial risks brought about by floods, storms and wildfires, and
demonstrated that leverage could magnify direct damage. Xiong et al. (2025) measured the
abnormal returns of stocks and studied the financial impact of extreme weather on the
logistics industry. Lamperti et al. (2021) studied policy tools for climate-related financial
risks. This was a problem different from the enterprise-level computing proposed in this
article.
Therefore, the gap is concrete. Existing quantification indicates that climatic conditions
and physical hazards have economic significance. It will not provide logistics users with a
table where one event is divided into revenue, operating costs, inventory and assets, leaving
each assumption on the page.
2.3 Climate condition in logistics
Many scholars studied the impact of climate on logistics and transportation. Becker et al.
(2018) examined the climate risks of ports and the supply chains using them. They pointed out
that damage and disruption had been observed. Izaguirre et al. (2021) evaluated how climate
change affects global port operations. Verschuur et al. (2023a) demonstrated that port
shutdowns pose risks to trade and broader economic activities, including those moving away
from closed ports. Verschuur et al. (2023b) separated the damage to port infrastructure from
the associated trade and logistics losses. They found that tropical cyclones are a major cause of
infrastructure risks.
The case evidence provided the operational variables. Zhang et al. (2020) estimated the
economic impact of typhoons on port operations in China. Tian et al. (2023) estimated the
regional losses caused by disruptions to coastal ports in China due to typhoons. Their key
research areas were duration, wind force and affected ports. Yang et al. (2018) evaluated
adaptation measures through risk and cost assessment. Wang and Zhang (2018) and
Randrianarisoa and Zhang (2019) studied adaptive investment in ports when facing disasters
9
and competition, including investment timing. Leon-Mateos et al. (2021) established the port
resilience index to compare exposure and adaptability.
Beyond the port boundaries, extreme weather still had a significant impact on the supply
chain. Ghadge et al. (2020) regarded climate change as a supply chain risk and called for efforts
to link hazards with performance. Er et al. (2021) modeled the impact of climate on
performance. Malik et al. (2022) demonstrated the cascading effects of extreme weather through
supply chains. Bag et al. (2023) placed adaptation and disaster risk reduction within the supply
chain network. Touloumidis et al. (2025) reviewed weather disruptions in transportation and
logistics and pointed out the implementation gap between understanding the hazards and taking
action.
Climatic conditions are described in terms of days, locations and physical damages.
Financial impacts can be described by revenue, costs, inventory and assets. For the logistics data
provided by the mentor, this paper combines these two descriptions and uses a language model
to ensure that the data is accurately analyzed and applied.
Section 3 Research methodology
3.1 Framework
Figure 1 is the path. The climate condition block records the event type, date, location,
duration and a simple measurement of intensity, such as wind or rainfall. The operation
disruption block records the affected ports, warehouses, routes or inventories, and the
operational quantities such as throughput or freight volume. The financial impact group
translates these quantities into four impacts, including revenue loss, additional operating
costs, inventory loss, and asset repair or impairment. The large language model block receives
the completed table and any text provided with the data, and returns a short statement. The
traceability check sends this statement back to the table.
(换图)
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Figure 1. Pathway from climate condition to a traceable disclosure draft.
Boundaries are part of data differentiation. Only physical climatic conditions are
included in the calculation. It only report the impacts related to the operational and financial
areas of supply. Stock prices, credit spreads and trade losses across the entire industry are still
in the literature review, which are not outputs.
3.2 Quantification
Each rule is written so that an assumption can be seen. Revenue loss equals disruption
days multiplied by affected daily volume and by unit margin. Additional operating cost equals
the extra transport, overtime, energy or recovery cost in the data. If the data give only a unit
cost, that unit cost times disruption days is used and labelled. Inventory loss equals affected
inventory value multiplied by a loss ratio. Asset impact equals repair cost, or asset value
multiplied by a stated reduction in recoverable amount, when a damaged facility is identified.
A term that is not in the data is marked as an assumption, and at least two values are
reported for it. The baseline uses ordinary operating days. The climate scenario replaces the
affected days with the event. Sensitivity analysis varies disruption days and the loss ratio. A
spreadsheet or a short Python script is sufficient, because the result to be defended is the
pathway and the audit.
3.3 Case data
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The case is the logistics setting in the data provided by the supervisor. The firm is not
chosen in this proposal, and public reports will not be scraped to stand in for those data. When
the file arrives, the event with the most complete link from climate condition to operations
and financial amounts will be the main case. If several events are present, one acute event will
be analysed in full. The others will be used only as a brief comparison.
Observed fields and assumed fields will be listed separately. This keeps the case
accountable. The source of the records is the supervisor. Any ratio that is extended beyond the
records will be identified as an extrapolation and discussed with the supervisor before it is
treated as a result. Confidential items will stay inside the dissertation use agreed with the
supervisor.
3.4 Language-model procedure and evaluation
The model has three restricted tasks. It may extract the event, location and duration
from text included in the data. It may assign each calculated impact to one of the four
financial items. It may draft a short disclosure paragraph. The prompt will require every
amount to carry the row identifier of the calculation table. An amount that is not in the table is
an error, even if the sentence is fluent (Leippold, 2023).
Evaluation uses the same table as the reference. Traceability is the share of numeric
claims in the draft that match a row. Coverage records whether revenue, operating cost,
inventory and assets are mentioned when the table contains them. A short paragraph written
by hand from the same table will be the comparison, so that omissions and added figures can
be seen. The draft is a support for explanation. It is not presented as a substitute for
professional reporting.
Section 4 Research plan and milestones 使用甘特图来展示你的研究计划和
时间表
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Figure 2 sets the work from November 2026 to June 2027. Literature and this proposal
occupy November and December. The variable list, formulas, checklist and prompt are
prepared in December and January, so that the design does not wait on the file. Receipt and
cleaning of the supervisor’s data are planned for January and February. Quantification and
scenarios follow in February and March. The draft and the traceability check are planned for
March and April. Writing runs from March to June, with revision at the end. If the data arrive
later, the formula file and the audit procedure can still be finished, and the case will move
without a change in the research question.
Figure 2. Research plan, November 2026 to June 2027.
The milestones are: proposal submitted; calculation template ready; data received;
main scenario results completed; disclosure draft checked against the table; full dissertation
draft completed.
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