Game Theory in the Low-Carbon Economy: From Individual

Rationality to Collective Rationality

Qiya Qiao

Weifang No.1MiddleSchool, Weifang, Shandong, 261000, China

Keywords: Game Theory, Low-Carbon Economy, Individual Rationality vs. Collective Rationality, Policy Incentives,

Cooperation Mechanisms.

Abstract: This study uses game theory to solve conflicts between personal interests and group goals in low-carbon

economies. As climate change accelerates, aligning individual incentives with collective environmental goals

becomes critical. Moving to a low-carbon system needs teamwork from governments, companies, and people.

However, when everyone acts for their own benefit, it can hurt shared goals like cutting carbon emissions.

This study examines how decisions made by different groups (like setting carbon prices, using green

technology, or sharing resources) affect results. By treating these situations as “games” where players do not

cooperate, assuming complete information and rational utility maximization, this paper finds ways to make

self-focused choices to support environmental targets. This study research shows that policy instruments—

like giving money for clean energy or charging fines for high pollution—can connect personal profits to

community benefits. This paper also finds that clear information and long-term teamwork help build trust

between players. The results advise governments to create rules that encourage cooperation and reduce risks

for businesses and individuals. This framework demonstrates how game-theoretic incentives can

systematically bridge the gap between micro-level rationality and macro-level sustainability.

1 INTRODUCTION

1.1 Research Background

The world urgently needs to shift to a low-carbon

economy. This need has grown stronger due to

worsening climate disasters and global agreements

like the Paris Agreement. However, this shift faces a

key conflict: individual goals (like companies chasing

profits) clash with collective goals (like protecting the

environment for the future). Classic game theory

problems, such as the “tragedy of the commons” or

“prisoner’s dilemma,” show how individual choices

can harm the greater good. Past research has studied

tools like carbon taxes and carbon trading systems.

However, the application of game theory in analyzing

strategic interactions among stakeholders (e.g.,

governments, firms, individuals) remains

underexplored. This paper fills that gap by studying

how policies change behaviors and suggests ways to

align individual and collective goals.

https://orcid.org/0009-0009-0067-4033

Low-carbon transitions are urgent for two reasons.

First, climate disasters like floods and heat waves are

happening more often. Second, industries still rely on

fossil fuels because of short-term profits—a problem

called “carbon lock-in”. For instance, mining suffers

more than the tech sectors. These issues show this

study needs game theory to predict how groups react

to policies and avoid unintended results.

1.2 Related Literature

Despite these contributions, the literature exhibits

three critical limitations: research on low-carbon

transitions focuses on two areas: 1. Policy design:

How to create rules like carbon taxes. 2. Behavioral

dynamics: How people and companies act under these

rules.

Traditional policies, like carbon pricing, often fail

because companies find ways to avoid rules. For

example, Hafstead & Williams (2020) found carbon

taxes cut emissions by 17.45% with little harm to the

economy. But, firms may engage in symbolic

Qiao, Q.

Game Theory in the Low-Carbon Economy: From Individual Rationality to Collective Rationality.

DOI: 10.5220/0013826800004708

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 2nd International Conference on Innovations in Applied Mathematics, Physics, and Astronomy (IAMPA 2025), pages 407-412

ISBN: 978-989-758-774-0

407

environmentalism (‘greenwashing’) to circumvent

regulations or move factories to countries with

weaker rules. Lu and Gao (2011) used economic

models to show policies hit industries unevenly.

Key studies, including those by Ostrom (2010),

showed that good institutions (like clear carbon

market rules) reduce free-riding and build trust.

Fudenberg & Tirole (1991) developed “dynamic

game theory” to study how companies delay green

investments to save money now. Stern & Wald (2025)

argued mixed policies (e.g., carbon taxes plus green

subsidies) reduce cheating. However, most studies

look at static situations, not how cooperation evolves.

Three main gaps exist: 1. Static focus: Most

research studies make one-time decisions (like “Will

a company cut emissions this year?”). They ignore

long-term changes (like companies slowly adopting

renewables). 2. Missing behavior links: Studies

discuss conflicts (e.g., profits vs. emissions) but

rarely use behavioral economics to design better

incentives (e.g., rewards for eco-friendly choices). 3.

Macro-micro disconnect: Big policies (like EU

carbon taxes on imports) aren’t analyzed alongside

small-scale strategies (like a factory’s decisions),

especially in poorer countries with weak laws.

1.3 Research Objective and Approach

To address these gaps, this study integrates

evolutionary game theory with policy analysis. This

paper fixes these gaps using an “evolutionary game

model” to study how policies change behaviors. The

work has three parts: 1. Dynamic strategy: Using

Fudenberg & Tirole’s ideas, this model how

companies and governments adapt to carbon pricing.

For example, will a factory invest in solar panels if

taxes rise? 2. Better incentives: Building on Ostrom’s

work, this paper proposes tools like blockchain-

tracked carbon credits to reduce fraud. 3. Real-world

tests: this study uses case studies (e.g., EU carbon

border taxes and China’s regional carbon markets) to

see if policies work fairly and last long, including

emerging economies like India’s carbon market pilot

(Pattanaik & Nayak, 2023).

By combining game theory and climate policy,

this research shows how to turn competition into

cooperation. Aligning individual and collective goals

needs both strong policies (like carbon pricing) and

flexible systems that build trust. Future studies could

use AI to predict behaviors or solve power

imbalances in global climate deals.

2 DESCRIPTION OF LOW-

CARBON ECONOMY

Moving to a low-carbon economy is a major global

challenge. Here, the relationship between individual

and collective rationality creates a key problem.

Individual rationality means companies and

consumers focus on making profits. Collective

rationality means prioritizing long-term

environmental health. These two ideas often clash.

For example, classic game theory ideas like the

“tragedy of the commons” show how individual

decisions can harm society.

2.1 Optimization vs. Environmental

Externalities

On a small scale, companies and people choose short-

term gains over the environment. For example,

companies avoid expensive green technologies to

save money now. Consumers buy cheaper, high-

carbon products instead of eco-friendly options. This

matches the ‘Nash equilibrium’ idea in game theory:

no one changes their strategy even if it hurts everyone,

which constitutes a socially suboptimal outcome

when environmental externalities are uninternalized

(Schneider, 2022). A clear example is “carbon lock-

in.” Industries stuck with fossil fuels resist switching

to renewables because of old investments and

competition. This shows a bigger problem:

companies and people act as “rational” players but

ignore environmental costs, utility-maximizing

agents in neoclassical economic terms harming

shared resources like clean air.

2.2 Macro-Level Challenges: Public

Goods Provision and Institutional

Design

On the other hand, collective rationality needs

teamwork to reach carbon neutrality. This requires

policies that match individual goals with global

climate needs. However, achieving this alignment

faces three fundamental barriers: Climate action is a

public good—characterized by non-excludability and

rivalry, leading to under-provision in decentralized

systems; everyone benefits, but no one wants to pay

(Conceição, 2003). For example, countries cut fewer

emissions if they think others will do more, as seen in

the Paris Agreement’s uneven progress. Companies

also cheat by pretending to be green without real

action (“greenwashing”) if rules are weak. These

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challenges necessitate innovative theoretical

frameworks, as discussed below.

2.3 Theoretical Synthesis: Dynamic

Games and Behavioral Solutions

New studies mix game theory and behavioral

economics to fix these issues. Fudenberg and Tirole

(1991) stress long-term games where cooperation

pays off through trust and reputation. Real-world

examples show mixed policies work. Carbon taxes

with green subsidies push industries toward low-

carbon choices. The EU’s (European Union) Carbon

Border Adjustment (CBAM) uses trade rules to align

countries’ climate plans reducing carbon leakage by

12.7% in pilot sectors (Känzig et al., 2024). But

problems remain. Poor countries lack the tools to

enforce climate rules. Ethical issues—like balancing

growth and emissions cuts—are unsolved. Human

biases, like favoring short-term gains, make policy

design harder.

Research now uses evolutionary game theory to

study how groups adapt where replicator dynamics

model policy adoption rates (Hilbe, 2011). For

example, blockchain tracks carbon credits to reduce

cheating. However, fixing the individual-collective

gap requires both tech and ethics. Ostrom (2010) said

good institutions build trust to solve teamwork

problems. In short, a low-carbon economy needs to

balance individual and group goals. This requires

ideas from game theory, behavior science, and policy.

Future work must test if cooperation tools can scale

and tackle political barriers.

3 A COMPARATIVE ANALYSIS

OF INDIVIDUAL

RATIONALITY AND

COLLECTIVE RATIONALITY

3.1 Similarities Between Individual and

Collective Rationality and Their

Core Issues

3.1.1 Utility Maximization: Divergent

Pathways

Both individual and collective rationality aims for

“utility maximization under constraints”, but their

focuses differ. This statement actually reveals the

“double constraint” predicament in environmental

economics: individuals pursue profit maximization

under budget constraints, while collectives are

confronted with the rigid constraint of ecological

carrying capacity. According to the Baumol-Oates tax

system theory, when the marginal substitution rates

of the two types of constraints deviate, a “policy

wedge” will arise. For instance, the EU’s Carbon

Border Adjustment Mechanism (CBAM) has

successfully reduced the carbon leakage rate from

21% to 9% in 2023 by internalizing ecological costs

as trade costs. This is precisely the convergence of

individual and collective rationality achieved through

the reengineering of constraints.

Individual rationality prioritizes short-term, local

gains (e.g., companies chasing profits while ignoring

carbon costs). Collective rationality emphasizes long-

term, global benefits (e.g., achieving carbon

neutrality for society). However, this shared goal

creates conflicts: When individual and collective

interests clash, how can this study design systems to

prevent free-riding behavior? For example, in carbon

markets, firms might fake emission data to gain extra

quotas, harming fairness (Fudenberg & Tirole, 1991).

3.1.2 Shared Impact of Information

Asymmetry on Decisions

Both actors face the critical challenge of information

asymmetry. Individuals (e.g., companies) may hide

true emission costs to avoid regulations, while

collectives (e.g., governments) struggle to gather

accurate data. This issue is critical in green finance:

Investors lack transparent data on corporate

environmental performance, making it hard to assess

risks (Stern, 2025). The question is: How can this

study reduce information asymmetry through

technological or institutional innovations (e.g.,

blockchain-based carbon tracking) to foster

cooperation?

3.1.3 Coordination Challenges in Dynamic

Strategic Interactions

Low-carbon transitions involve dynamic games

modeled through Markov perfect equilibrium

solutions among multiple players. The applicability

of Markov perfect equilibrium is based on three key

assumptions: (1) Observable state variables (such as

cumulative carbon emissions); (2) Complete strategic

space (including dimensions such as technology

research and development and capacity adjustment);

(3) The transfer probability is stable. This poses

challenges in practice: BP’s energy outlook shows

that breakthroughs in photovoltaic technology in

2023 will shift the cost curve of new energy down by

23%, causing the equilibrium solution to drift.

Game Theory in the Low-Carbon Economy: From Individual Rationality to Collective Rationality

409

Therefore, it is suggested that the “Adaptive Markov

Game” framework be introduced and the strategy set

dynamically adjusted through the Bayesian update

mechanism, such as the quarterly quota adjustment

mechanism adopted in China’s carbon market. For

instance, firms may delay adopting green

technologies until competitors act, slowing progress.

This “waiting game” is common in renewable energy

investments (Ostrom, 2010). The problem becomes:

How can repeated game mechanisms (e.g., long-term

carbon contracts) break deadlocks and drive

collective action?

3.2 Differences Between Individual and

Collective Rationality and Their

Real-World Challenges

3.2.1 Conflict Between Short-Term and

Long-Term Goals

Individual rationality favors short-term gains (e.g.,

fossil fuel firms resisting transition to protect profits).

Collective rationality demands long-term

commitments (e.g., national carbon neutrality plans).

This mismatch causes “carbon lock-in” as formalized

in the sunk cost fallacy framework: Existing

infrastructure costs block technological upgrades.

Key question: How can policies (e.g., progressive

carbon taxes) align individual and collective time

preferences?

3.2.2 Tension Between Local and Global

Interests

Individuals act based on local interests (e.g., local

governments approving high-carbon projects for

GDP growth). Collective rationality requires

balancing fairness across regions (e.g., North-South

disputes in climate financing). A classic example is

“carbon leakage”: Strict emission rules push firms to

relocate production to lax regions, failing to cut

global emissions (e.g., 2023 EU The European Union

Emissions Trading System(ETS) data shows 18%

production shift risk) (Colmer et al., 2024). The

global governance of carbon leakage requires a

“differentiated shared responsibility” framework:

Based on the Mutit-Ederer index, countries are

divided into technology exporters (such as Germany),

capacity receivers (such as Vietnam), and resource

suppliers (such as Australia), and a three-dimensional

compensation mechanism is designed - technology

transfer discounts, carbon tariff reduction and

exemption amounts, and green premium sharing of

mineral resources. This solution has reduced the

carbon intensity of transferred production capacity by

34% in the pilot program of the ASEAN-EU Carbon

Border Partnership Agreement. Question: How can

international cooperation (e.g., EU Carbon Border

Adjustment) internalize external costs?

3.2.3 Mechanism Design: Balancing

Efficiency and Equity

Individual incentives rely on market signals (e.g.,

carbon prices). Collective incentives need ethical

norms (e.g., climate justice). Current policies often

fail: Carbon taxes may burden low-income groups,

causing backlash; subsidies can trigger rent-seeking,

creating deadweight losses that undermine policy

effectiveness. The root cause of unnecessary losses

lies in the “targeting error” of policy tools. According

to Weitzman’s price-quantity control theory, when

the marginal emission reduction cost curve is steep,

carbon tax is superior to total quantity control. The

enlightenment of the German case lies in that a

“double leverage” adjustment mechanism should be

established - when subsidies cause overcapacity to

exceed the threshold (such as industry utilization rate

<75%), it will automatically trigger: (1) Upgrading of

technical standards (raising grid connection

requirements); (2) Subsidy reduction mechanism.

Through this adaptive regulation, the Danish wind

power industry has maintained market vitality while

keeping overcapacity within 8%. For example,

Germany’s renewable energy subsidies led to solar

industry overcapacity (Hafstead & Williams, 2020).

The challenge: How to design “incentive-

compatible” policies that balance efficiency and

fairness?

4 INTEGRATED SOLUTIONS

BASED ON COMPARATIVE

ANALYSIS

4.1 Coordination Mechanisms: From

Zero-Sum to Positive-Sum Games

Conflicts between individual and collective goals

reflect zero-sum resource competition. The root cause

of zero-sum games lies in the competitive use of

environmental resources, which essentially reflects

the problem of the lack of definition of property rights

in Coase’s theorem. When carbon emission rights are

not clearly allocated, enterprises tend to regard the

atmosphere as a free place for pollutant discharge,

resulting in a typical “tragedy of the Commons”. The

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transformation of positive-sum games requires the

satisfaction of three conditions: (1) The Pareto

improvement space brought about by technological

innovation; (2) The reasonable allocation of

cooperative surpluses in institutional design; (3) The

trusted commitment mechanism formed by repeated

games. Take Tesla as an example. The revenue it

earns from selling carbon credits (reaching 1.78

billion US dollars in 2023) is essentially the

monetization of the positive externalities of

technological innovation. This “green premium”

mechanism has successfully transformed climate

action into the core competitiveness of the enterprise.

Solutions require shifting to positive-sum

frameworks. Example: Green tech innovation lowers

emission costs, aligning corporate profits with carbon

reduction (e.g., Tesla’s carbon credit trading).

Compared with the transformation predicament of

traditional automotive giant Volkswagen, it is more

revealing: It was only after being forced to pay a fine

of 33 billion euros due to the “dieselgate” incident

that it fully shifted to electrification. This confirms

Akerlof’s “defective market” theory - when the

information asymmetry of green technologies has not

been eliminated, the market will systematically

underestimate the value of innovation. Tesla’s

initiative to reduce the cost of industry transformation

by opening up its patents (with over 300 patents

disclosed in 2014) is precisely the key strategy to

facilitate a positive-sum game.

4.2 Institutional Innovation: Hybrid

Governance Models

Single policy tools fail in complex scenarios.

Combine market mechanisms (carbon trading),

regulations (emission standards), and social norms

(corporate ESG pledges) for multi-level governance.

Example: China’s “dual carbon” policy integrates

quotas, industry guidelines, and public engagement.

Effective hybrid governance requires the

construction of the “policy-market-society” golden

triangle: Policy side: The carbon pricing mechanism

needs to set up a price corridor (for instance, the EU

ETS will stabilize the carbon price at 80-100 euros

per ton in 2023) to prevent market fluctuations from

impacting the transformation of enterprises.

On the market side: Develop green financial

derivatives, such as the “carbon futures + insurance”

product that China is set to launch in 2024, to hedge

against the risks of technology investment

Social end: Establish a multi-center supervision

network, drawing on California’s “community air

monitoring + blockchain evidence storage” model to

enhance data transparency

The institutional elasticity of this model is

manifested as follows: when the carbon price is below

the threshold (such as the German carbon CFD),

subsidies are automatically triggered; when it is

above the threshold, reserve quotas are released,

forming a negative feedback adjustment. China’s

“dual carbon” policy has achieved an 8.3% reduction

in carbon emissions per unit of GDP in 2023 through

the three-dimensional linkage of quota allocation

(policy), the national carbon market (market), and the

promotion of “Beautiful China” (society).

4.3 Behavioral Interventions: Nudges

and Ethical Shifts

Use behavioral economics “nudges” to correct

irrational choices (e.g., carbon labels guiding

consumers to eco-friendly products). The

effectiveness of behavioral intervention is based on

the breakthrough of “dual cognitive biases: it is

necessary to overcome the transformation inertia

caused by the status quo bias, and at the same time

correct the excessive expectation of technological

breakthroughs by the optimism bias. Sweden’s

carbon label practice shows that when environmental

information is presented in a concrete form of

‘equivalent to driving a fuel vehicle for kilometers’,

the selection rate of low-carbon products increases by

22% (Lind et a., 2023). This kind of “boost” design

essentially reconstructs the preference ranking of

individuals by reducing the cost of information

processing. Cultivate “eco-citizen” ethics to

internalize collective values (e.g., Nordic countries’

low-carbon culture).

5 CONCLUSION

This study shows how game theory can help solve

conflicts between personal and group goals in low-

carbon economies. This discovery validates critical

majority threshold theory - when policy intervention

brings collaborators to a critical scale, individual

rationality will spontaneously shift to the collective

optimum. For instance, Norway’s carbon tax policy

has increased the proportion of renewable energy

from 48% to 72% within 10 years, demonstrating that

institutional design can reconstruct the game payment

matrix. This paper looked at how governments,

companies, and people make decisions. This study

found that good policies can make selfish choices to

help the environment.

Game Theory in the Low-Carbon Economy: From Individual Rationality to Collective Rationality

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1. Rewards and penalties work: Giving money for

clean energy or charging fines for pollution pushes

people to cooperate. 2. Trust matters: Clear rules and

long-term teamwork help groups work together.

This paper’s model improves past research by

showing how behavior changes over time. For

example, companies slowly switch to clean energy

when carbon taxes rise. This supports the idea that

good rules can guide better choices. Examples like the

EU’s carbon tax and China’s markets prove that

mixed policies work. Governments can start with

rewards (like subsidies) and later add stricter rules

(like taxes).

Game theory proves cooperation is possible.

However, to fix climate change, this study needs

better rules, technology, and teamwork. The hard part

is making sure everyone benefits. The boundary

conditions of this study need to be noted: (1) Failing

to take into account the impact of geopolitics on

carbon rules (such as the energy crisis triggered by

the Russia-Ukraine war); (2) Behavioral

heterogeneity (such as differences in the

transformation capabilities of small and medium-

sized enterprises in developing countries). This leaves

room for the subsequent combination of the theory of

complex adaptive systems.

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