A Linear Programming-Based Analysis of Dual-Carbon

Implementation in East China’s Major Industries

Yongshi Fang

1,* a

, Xiwen Liu

and Donghao Tu

RCF Experimental School, Beijing, 100012, China

Chengdu No. 7 High School, Chengdu, Sichuan, 610041, China

Shanghai United International School Hefei Campus, Hefei, Anhui, 230071, China

＊

2 3

Keywords: Dual-Carbon, Challenge, Government, Enterprise.

Abstract: In today's social environment, dual carbon has become a hot topic. The definition of dual-carbon is the

abbreviation of carbon peak and carbon neutral. The study is based on how the three major industries in East

China implement the dual-carbon goal by two methods to maximize the economic harvesting basin. The study

incorporates a linear programming approach. A set of optimization policies is proposed to address the

characteristics of the three major industries in the region (e.g., large carbon emissions from traditional

industries and almost zero carbon emissions from modern services), and the results of the study can provide

policy references as well as technical support for the region and other similar regions to achieve the dual-

carbon goal. Some limitations, contributions, and implications of the study are identified in the course of the

discussion. The development of financial products to establish a carbon trading market to realize the

development of the whole economy and promote the mutual benefits of carbon emission reduction. The dual-

carbon program needs to be realized by the government, enterprises, and the market with continuous

adjustments.

1 INTRODUCTION

East China is promoting the dual-carbon goal to

optimize the industrial structure, seeking to maximize

the economic efficiency basin and control carbon

emissions. The government also needs to consider the

operating costs, labor requirements, and resource

constraints of each industry to ensure sustainable

development. There are three main types of industries

in the region. First, traditional industries have the

highest carbon emissions, the best economic

performance, and the lowest labor costs. Second, the

clean energy industry has low operating costs, low

carbon emissions, and medium economic

performance. Third, the modern service industry has

the highest economic efficiency, zero carbon

emissions, and high labor costs. The research on dual-

carbon goals describes the background of dual-carbon

goals, analyzes the challenges and opportunities

https://orcid.org/0009-0008-6199-7402

https://orcid.org/0009-0001-3529-8033

https://orcid.org/0009-0001-4033-4799

brought by dual-carbon goals, and the realization path

to achieve dual-carbon goals. The study of the dual-

carbon strategy puts forward the analysis of the

impact of the dual-carbon strategy on the real

economy, and proposes that the industry focuses on

the modernization of the real economy's mode of

production and life; the governmental measures focus

on the financial, legal and regulatory, standard system

and institutional reforms. Studies related to the

development of a dual-carbon economy reveal the

background of the proposed dual-carbon goal while

analyzing the challenges and opportunities brought

about by the dual-carbon goal and further proposing a

path to achieve the dual-carbon goal.

This paper studies how the three major industries

in East China implement the dual-carbon target,

aiming to propose a set of industrial structure

optimization strategies based on carbon emission

constraints. It provides policy reference and technical

Fang, Y., Liu, X. and Tu, D.

A Linear Programming-Based Analysis of Dual-Carbon Implementation in East China’s Major Industries.

DOI: 10.5220/0013815600004708

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 145-151

ISBN: 978-989-758-774-0

145

support for this region and other similar regions to

realize the dual-carbon target.

2 DEFINITION AND

DEVELOPMENT OF

DUAL-CARBON

2.1 Classification of Dual-Carbon

“Dual-Carbon” is the abbreviation of “Carbon Peak”

and “Carbon Neutral”, and it is an important strategic

goal proposed by China to realize the green and low-

carbon development. It is an important strategic goal

proposed by China to realize green and low-carbon

development. Peak Carbon refers to a point in time

when carbon dioxide emissions reach a historical

high, after which they gradually decline. This is the

historical inflection point of carbon emissions from

increasing to decreasing, marking the decoupling of

economic development and carbon emissions. Carbon

neutrality refers to the offsetting of carbon dioxide or

greenhouse gas emissions directly or indirectly

through tree planting, energy saving, and emission

reduction within a certain period of time so as to

realize “net-zero emissions”.

2.2 China's Historical Process of

Practicing Dual-Carbon

In 2015, the Opinions on Accelerating Energy

Consumption and Emission Reduction put forward

the goal of “dual-control” (controlling the total

amount of energy consumption and total amount of

carbon dioxide emissions). 2016 Opinions on the

Implementation of the National Strategy for

Addressing Climate Change set the goal of carbon

peaking by 2030, with the proportion of non-fossil

energy consumption reaching 20%. 2017 saw the

proportion of non-fossil energy consumption reach

20%. In 2017, the “Opinions on Promoting Green

Development” was issued to promote the energy

revolution and green transformation of industries, and

in 2020, President Xi Jinping announced China's goal

of reaching peak carbon by 2030 and carbon

neutrality by 2060, and in 2021, the top-level design

of the dual-carbon “I+N” policy system was

introduced (the “Opinions on Integrating and Fully

Carrying Out the New Development Idea”). In 2021,

the top-level design of the dual-carbon “I+N” policy

system will be issued (“Opinions on Doing a Good

Job in Peak Carbon Achievement and Carbon

Neutrality Work”, “Action Plan for Peak Carbon

Achievement by 2030”). In 2024, detailed policies

will be issued, such as “Guiding Opinions on

Accelerating the Promotion of Manufacturing

Industry's Greening” and “Action Plan for Energy

Saving and Carbon Reduction in the Years of 2024-

2025”.

2.3 Role and Challenges of

Dual-Carbon

The Chinese government promotes green

transformation through policy guidance (e.g.

manufacturing greening, energy saving, and carbon

reduction actions), technological innovation, and

international cooperation. Typical examples:

Chenming Paper reduces carbon emissions in its

supply chain through green technology (Sohu.com);

the China Meteorological Administration (CMA)

points out that dual-carbon is the “golden key” to

sustainable development.

The transformation of energy structure constitutes

the main difficulty in switching from chemical energy

to clean energy. Disputes over the allocation of

responsibility for emission reduction stem from

uneven regional development and need to be resolved

through regional and industry coordination.

Technological innovation: Low-carbon technology,

R&D and industrialization are insufficient (e.g.,

carbon capture, hydrogen energy) are technological

innovations. The main element of international

competition is the challenge of global green

technology standards and trade barriers.

There are some coping strategies here, such as

strengthening top-level design, coordinating regional

and industry synergies, deepening the energy

revolution and investment in technological innovation

(e.g., for the low-carbon transformation of the

petrochemical industry, see the White Paper on

China's Petrochemical Industry in the Context of

Dual-Carbon 2024); and perfecting the market

mechanism (e.g., carbon trading, green finance).

3 METHODS

3.1 Mathematical Programming Model

for the Tertiary Industry: A Linear

Programming Template

Assume that the production decision variables of the

three industries are (x_1, x_2, x_3), that is, n=3. To

maximize profits, the objective function is:

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146

Maximize 𝑍 =

∑

𝑐



𝑥







(1)

The parameters satisfy:

∑

𝑎

,

𝑥







≤𝑏





𝐿𝑎𝑏𝑜𝑟 𝐶𝑜𝑛𝑠𝑡𝑟𝑎𝑖𝑛𝑡𝑠



(2)

∑

𝑎

,

𝑥







≤𝑏





𝑅𝑒𝑠𝑜𝑢𝑟𝑐𝑒 𝐶𝑜𝑛𝑠𝑡𝑟𝑎𝑖𝑛𝑡𝑠



(3)

∑

𝑎

,

𝑥







≤𝑏





𝐶𝑎𝑟𝑏𝑜𝑛 𝐸𝑚𝑖𝑠𝑠𝑖𝑜𝑛 𝐶𝑜𝑛𝑠𝑡𝑟𝑎𝑖𝑛𝑡𝑠



(4)

𝑥



≥ 0 𝑓𝑜𝑟 𝑎𝑙𝑙 𝑖



𝑁𝑜𝑛 − 𝑛𝑒𝑔𝑎𝑡𝑖𝑣𝑖𝑡𝑦 𝐶𝑜𝑛𝑠𝑡𝑟𝑎𝑖𝑛𝑡𝑠



(5)

As shown in Table 1, this is the parameter

description and instance value

Table 1: The parameter description and instance value

Symbol Meaning Example Value

𝑐



Unit product

rofit



12, 7,10



𝑎

,

Resource

consumption

coefficient





1, 1,1





30, 10,0



2, 1,3





𝑏



Total

resources



300, 3000,500



3.2 Data Preparation Methodology

3.2.1 Data Source Selection

Government statistical databases (such as the

National Bureau of Statistics of China and the OECD

official data platform) can obtain macro data such as

industry capacity ceiling through public channels.

The data sources are collected by government

departments through standardized processes such as

sampling surveys and direct reporting by enterprises.

They are regularly audited by third parties and comply

with international statistical standards. They have

legal effect and reference value for policy

formulation.

Annual reports can be viewed through official

platforms such as the U.S. Securities and Exchange

Commission's EDGAR system or the China National

Enterprise Credit Information Publicity System.

These audited financial reports disclose detailed

micro-operation data such as raw material costs and

management expenses. Information disclosure by

listed companies is subject to the Securities Law and

other regulations, and they must bear legal liability for

fraud.

The standard values of industry technical

coefficients can be referred to the annual white papers

released by McKinsey, Boston Consulting Group, and

other institutions. These reports are based on field

surveys of leading global companies and use input-

output models and machine learning algorithms for

cross-validation. Their methodology has been peer-

reviewed by academic journals such as Nature and has

been incorporated into the industry benchmark

indicator system by international organizations such

as the World Bank.

3.2.2 Sample Selection Strategy

In the sample selection strategy, stratified sampling is

suitable for the analysis of industrial portfolios with

significant heterogeneity. For example, the China

National Survey Database (CNIS) covers industrial

enterprises in 31 provinces across China through a

multi-stage probability proportional sampling

method. Its sample frame is dynamically updated

based on the economic census directory, and the

weight distribution refers to the proportion of industry

added value and is calibrated through the chi-square

test, which can effectively reduce the estimation bias

between heterogeneous groups.

Cluster sampling is more suitable for regional

industrial cluster research scenarios. For example, the

World Bank Development Indicators Database uses a

geographic grid clustering algorithm to define the

collection of enterprises within a 30-kilometer radius

of national special economic zones and industrial

chain supporting facilities as sampling units. The data

comes from the location entropy and supply chain

coupling indicators reported by the statistical bureaus

of various countries in a standardized manner. It has

been verified by OECD-DAC for consistency and

supports cross-national panel data comparison, which

can systematically characterize the spatial

agglomeration effect and coordination cost

characteristics of industrial clusters.

3.2.3 Verification Tool Chain

The verification toolchain includes multiple tools,

each with different purposes and documentation

support. Python-scipy-linprog is a tool for library

function verification, LpSolve is used for model

verification, Gurobi is a commercial-grade

optimization tool for high-performance solvers, and

Pyomo is an open source modeling tool for a variety

of optimization problems.

3.2.4 Operation Results

When the input data and sample parameter values are

the same, the optimal output is 40.0 for agriculture,

180.0 for industry, 80.0 for services, and the optimal

A Linear Programming-Based Analysis of Dual-Carbon Implementation in East China’s Major Industries

147

value is 2540.0. Through data visualization, these

results can be displayed more intuitively, helping us

better understand the output distribution of each

industry and its contribution to the overall optimal

value.

Figure 1: The simplex method's iterative process (Photo/Picture credit: Original).

Figure 1 shows the iterative process of solving the

linear programming problem using the simplex

method, specifically presenting the changes in the

objective function value and the changes in output

caused by the three variables of agriculture, industry,

and services.

3.3 Results Analysis

In a region committed to achieving carbon peak and

carbon neutrality goals, optimizing the industrial

structure to maximize economic benefits and control

carbon emissions is an important task. By establishing

a linear programming model and solving the optimal

industrial configuration plan, it has drawn the

following important insights: In terms of industrial

upgrading, the proportion of clean energy industry

and modern service industry has increased

significantly, reflecting the transformation trend from

traditional industries with high carbon emissions to

modern industries with low carbon emissions; in

terms of resource utilization, the optimized resource

allocation has made the total production, carbon

emissions and labor reach the upper limit, making full

use of existing resources; in terms of sustainable

development, by increasing the proportion of clean

energy and modern service industries, it have

successfully achieved maximization of economic

benefits while controlling carbon emissions.

Based on this, the following policy

recommendations are put forward: First, increase

support for the clean energy industry, formulate

preferential policies, encourage enterprises to invest

in the research and development and application of

clean energy technologies, and enhance the market

competitiveness of the clean energy industry; second,

promote the green transformation of traditional

industries, provide financial subsidies and technical

support, and encourage traditional industrial

enterprises to adopt low-carbon technologies and

clean production processes to reduce carbon

emissions; in addition, improve the carbon trading

market mechanism, establish and improve the carbon

trading market, regulate carbon emissions through

market mechanisms, and stimulate the enthusiasm of

enterprises to reduce emissions; finally, promote the

development of modern service industries, cultivate

new forms of modern service industries, promote the

development of industrial structure towards high

added value and low emissions, and enhance the

sustainability of the overall economic structure.

4 DISCUSSION

4.1 Study Limitation

By constructing linear programming models for

different industries, this study provides a scientific

and effective method for optimizing the industrial

production structure to control carbon emissions and

maximize economic returns. However, the research

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data comes from the macro data obtained from

stratified sampling and cluster sampling in a certain

region, which is limited to the industry situation in a

single region. Therefore, the conclusion cannot meet

the applicability of industries in different regions and

the universality of the research results Secondly, the

constraints on labor, resources and carbon emissions

in the model cannot be matched with government

policy intervention in specific regions and market

economy fluctuations, which cannot reflect the

significant impact of market fluctuations on industrial

decision-making shown in international experience,

which affects the feasibility of this study. Thirdly, the

dual carbon plan is advancing rapidly, and the

development of various technologies may change the

relationship between carbon emissions and economic

benefits after the optimization of industry types, but

this study cannot predict and simulate the impact

caused by the emergence of different technologies.

4.2 Improvement & Suggestion

To ensure that major enterprises further promote and

strengthen the optimization of industrial structure

under the dual carbon goals, this paper will put

forward the following suggestions for the government

and enterprises:

4.2.1 Government Policy Recommendations

The government needs to improve and innovate the

system according to the local situation, and at the

same time need to improve the scientificity, rigor and

implementability of laws and policies related to

carbon emissions, the government should increase its

support for the clean energy industry, providing

energy subsidies and carbon tax policies for

enterprises based on clean energy industry (Zhang, et

al., 2023). And encourage enterprises to research and

develop high-tech clean energy with high

performance and low energy consumption (Wang, et

al., 2020). And then ， government can let some

enterprises with relatively complete production

structures to be the demonstration projects， Provide

suggestions for improvement to other clean energy

companies to increase the competitiveness of the

clean energy market; increasing demand for clean

energy products and improve the trading mechanism

of the carbon trading market (Guan, et al., 2022). This

will help more enterprises participate in carbon

trading and improve technological innovation

capacity in clean energy. For the modern service

industry, the government can encourage enterprises to

develop the digital economy, smart production, and

green finance through preferential fiscal and tax

policies (Zhou, et al., 2021). Compared with the other

two industries, the government should strictly manage

the carbon emissions of traditional industrial

enterprises, set carbon emission caps, and introduce

policies that require an increase in carbon taxes

beyond a specific emission level, so as to guide

traditional industrial enterprises to carry out green

transformation through economic means (Yao, et al.,

2020). At last, the government can suggest that

traditional industrial enterprises reduce the waste of

production resources through recycling, and carry out

technological transformation of energy conservation

and emission reduction, which can improve the

utilization value and efficiency of resources (Chen, et

al., 2019).

4.2.2 Communication Between Government

and Business

The government can formulate a communication

mechanism with enterprises to help enterprises

understand the relevant policies issued by the state at

the first time, ensure that the production objectives of

enterprises are in line with the national strategic

objectives, achieve information exchange, and help

enterprises to adjust and optimize their own industrial

structure according to the development trend of the

country (Chen, et al., 2022). At the same time,

universities can provide enterprises with the latest

technical support and innovative resources in the first

time, which improves the efficiency of achieving the

double carbon goal and ensures the sustainability of

industrial applications.

4.2.3 Companies Should Make Green

Transition

As the main body of carbon emissions, enterprises

should adapt to the relevant policies issued by the

government, transform the internal structure of the

industry according to the current environment and

situation, and actively promote the development of

low carbon emission goals. Enterprises can reduce

carbon emissions by increasing investment in

continuous improvement of production equipment,

reducing the use of chemical energy, increasing the

development and utilization of clean energy, and

improving the efficiency of resource reuse. Secondly,

each company can set up a carbon emission

monitoring system according to its own products,

which helps enterprises to understand and detect

which parts of the production process will produce

more carbon emissions and resource waste(Dong et

al., 2023). This can help enterprises to carry out point-

A Linear Programming-Based Analysis of Dual-Carbon Implementation in East China’s Major Industries

149

to-point optimization, improve production efficiency,

ensure the maximization of economic benefits, but

also reduce carbon emissions (Skrynkovskyy et al.,

2022).

5 CONCLUSION

According to the characteristics of the industrial

structure of traditional industry, clean energy industry

and modern service industry in a region, a linear

programming model is constructed in this study. With

labor, resources and carbon emissions as constraints,

it provides new ideas and optimized analysis on how

to maximize economic benefits for the industry. The

study shows that the industrial structure continues to

develop from high carbon emissions to low carbon

emissions, especially the proportion of clean energy

industry and modern service industry has increased

significantly, but the proportion of traditional industry

has decreased compared with the former. This trend

shows that the production volume, carbon emissions,

and labor allocation after the optimization of the

industrial structure have reached the upper limit, and

the resources are extremely well utilized. This

phenomenon reflects the unlimited potential of low-

carbon enterprises to optimize and rationalize the

allocation of resources.

It summarizes the goal of industrial structure

optimization as the goal of realizing the dual carbon

strategic policy of enterprises, optimizing their

internal structure to continuously develop in the

direction of low carbon and high added value. With

the constraints of carbon emissions and the

continuous reduction of resource waste,labor and

production through continuous adjustment to achieve

appropriate optimal allocation, and finally maximize

economic benefits. In addition, the study finds that the

proportion of clean energy industry and modern

service industry has increased, indicating that while

ensuring normal economic growth, effective control

of carbon emissions can lay the foundation for green

development and achieve sustainable industrial green

transformation.

This study provides enlightenment and policy

theoretical support and suggestions for how different

industries can optimize their economic benefits by

adjusting their industrial structure and types under the

background of carbon peaking and carbon neutrality.

The results encourage the government to increase

support for the clean energy industry, provide

subsidies and technical support for the green

transformation of traditional industries, and establish

a unified national carbon trading legal system.

expanding the industrial coverage of the carbon

market. Develop carbon financial products to

establish a sound carbon trading market, so as to

achieve a win-win situation that promotes the

development of the entire economy and achieves

carbon emission reduction goals. Therefore, the

results of this study can provide ideas and valuable

guidance for the government to establish green

economic policies, and help the government to

formulate more environmentally friendly economic

policies with the help of different industries to change

the way enterprises operate.

The two-carbon plan needs the continuous

adjustment and joint efforts of the government,

enterprises and the market to achieve. The

government should play a leading role, formulate

reasonable plans for the green transformation of

enterprises with a supervisory role. For enterprises,

they need to have the independent initiative of green

transformation, and improve their production

technology and innovation ability through continuous

research and development. In response to different

market conditions, various industries should combine

their own characteristics, take economic growth and

carbon reduction as the ultimate goal, and formulate

cooperation plans to achieve a common win-win

situation. Through the continuous improvement of the

industrial structure and system, China's dual carbon

development plan will be more perfect and lay a solid

foundation for the sustainable development of the

dual carbon structure.

AUTHORS CONTRIBUTION

All the authors contributed equally and their names

were listed in alphabetical order.

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