Bridging the Gap: Forecasting China’s Dual-Carbon Talent Crisis and Strategic Pathways for Higher Education

Abstract

China’s carbon peak and neutrality transition is critically constrained by the severe talent shortage and structural inefficiencies in higher education. This study systematically investigates the current status of “dual-carbon” talent cultivation and demand in China, leveraging annual “dual-carbon” talent cultivation data from universities nationwide. By applying the GM(1,1)-ARIMA hybrid forecasting model, it projects future national “dual-carbon” talent demand. Key findings reveal significant regional disparities in talent cultivation, with a pronounced mismatch between industrial demands and academic supply, particularly in interdisciplinary roles pivotal to decarbonization processes. Forecast results indicate an exponential growth in postgraduate talent demand, outpacing undergraduate demand, thereby underscoring the urgency of advancing high-end technological research and development. Through empirical analysis and innovative modeling, this study uncovers the structural contradictions between “dual-carbon” talent cultivation and market demands in China, providing critical decision-making insights to address the bottleneck of carbon-neutral talent development.

1. Introduction

Global greenhouse gas emissions reached 37.4 billion tons of CO₂ equivalent in 2023 [1], and the associated challenges of global climate change and environmental degradation have posed severe threats to humanity [2]. To address these climate challenges, China has put forward the strategic goal of “carbon peaking by 2030 and carbon neutrality by 2060” (hereafter referred to as the “dual-carbon” strategy) [3]. As a complex systematic project, the dual-carbon strategy demands coordinated efforts across energy, industry, economy, and society. However, the scientific framework for China’s dual-carbon strategy remains incomplete. Mainstream institutional estimates indicate that the implementation of this strategy will require exponential growth in market demand for science and technology, along with a surge in talent requirements [4]. Recognizing the critical role of high-quality dual-carbon talent, China has prioritized their cultivation since 2021 through policy documents such as the “Higher Education Carbon Neutral Science and Technology Innovation Action Plan” and the “Work Program for Strengthening the Construction of a Talent Cultivation System for Carbon Peak and Carbon Neutrality in Higher Education,” which have provided directional guidance for talent development [5,6]. Despite these efforts, China still faces a significant shortage of high-quality dual-carbon professionals.

Universities, as key hubs of scientific and technological innovation and talent cultivation, bear substantial responsibility in this regard [7]. They are expected to serve as primary bases for basic research and independent talent development. A report by the Ministry of Human Resources and Social Security [8] further underscores this urgency: labor market analysis reveals acute talent gaps, with over 65% of energy-intensive enterprises reporting shortages in specialized roles such as carbon auditors and CCUS engineers, while carbon finance positions remain undersupplied at less than 40%. Consequently, there is an urgent need to investigate the demand for high-level scientific and technological talent in Chinese universities and to develop targeted cultivation strategies under the dual-carbon framework.

Existing domestic research on universities’ contributions to achieving the dual-carbon goal has focused on five key dimensions: carbon footprint assessment of universities [9], low-carbon laboratory construction [10,11], integration of carbon monitoring into curricula [12], establishment of dual-carbon-related academic programs [13], and cultivation of postgraduate students’ innovative capabilities [14]. In terms of policy, scholars such as Wang Ruzhi and Cui Sup [15] have proposed an innovative “four-in-one” undergraduate education model aligned with dual-carbon objectives, while Jiang Yangfei and Xu Yuting [16] have emphasized the importance of integrating green and low-carbon concepts into university ideological and political education systems. Internationally, universities in Europe, the U.S., and Japan have pioneered interdisciplinary approaches to sustainable development, offering valuable insights for China’s dual-carbon talent cultivation. The European Union’s Green Deal framework [17] mandates that 70% of member universities deliver interdisciplinary climate education, integrating environmental science with law and economics through initiatives like Erasmus + exchanges, which have reduced regional skill gaps by 25% since 2020 [18]. In the U.S., industry-academia partnerships—such as Stanford University’s joint lab with Chevron on carbon capture technology—have aligned curricula with market demands, filling 40% of CCUS engineering positions within five years [19,20]. Japan’s policy-driven graduate programs, exemplified by Kyoto University’s Tokyo Stock Exchange-related carbon finance simulations, have trained 80% of the country’s carbon auditors since 2021 [21]. These models highlight the importance of systematic integration of STEM training, policy coordination, and industry collaboration. In contrast, China’s dual-carbon education remains fragmented: programs are concentrated in eastern provinces (e.g., Jiangsu and Beijing), with limited presence in western regions (e.g., Qinghai and Tibet). To address these gaps, this study synthesizes global practices to propose a hybrid strategy for China: establishing cross-regional dual-carbon innovation centers and embedding a policy–industry co-design mechanism inspired by Japan’s certification framework. For talent demand forecasting, commonly used methods include the gray forecasting model [22], BP neural network [23], and ARIMA model [24]. Given the challenges of large fluctuations and incomplete data, the gray prediction model has gained increasing attention, particularly in science and technology and talent supply forecasting, where it has demonstrated superior performance [25,26]. Prior studies have applied this model to forecast demand for healthcare [27], scientific and technological innovation [28,29], Yangtze River Delta culture [30], and coal industry talents [31,32,33,34].

In summary, this study addresses critical empirical gaps in understanding dual-carbon talent development in Chinese universities, where mismatches between educational outcomes and employment demands persist. While foundational strategies exist, key issues—including disciplinary development status, quantifiable supply–demand dynamics, and regional disparities—remain unclear. To bridge these gaps, we systematically map the distribution of dual-carbon disciplines, revealing structural inefficiencies and significant regional imbalances in China’s higher education system. Methodologically, we introduce a novel hybrid GM-ARIMA forecasting model, which reduces long-term prediction errors by 41% compared to the standard GM(1,1) approach, mitigating limitations related to small-sample volatility and external shock sensitivity. Empirical findings project a surge in dual-carbon talent demand from 360,000 in 2030 to over 1.45 million by 2060. Analysis further reveals severe regional polarization: 78% of relevant programs are concentrated in eastern provinces, contrasting with fewer than five institutions in western regions (e.g., Qinghai and Tibet). Based on these insights, we recommend prioritizing resource reallocation to western provinces, breaking down disciplinary barriers through integrated programs (e.g., environmental science combined with carbon finance), and aligning education with emerging skill needs. This empirically grounded study provides a foundation for synchronizing China’s talent pipeline with its 2030/2060 dual-carbon goals. The specific structure of the article is shown in Figure 1.

2. Data Collection and Research Methodology

2.1. Data Collection

Since the number of students trained in dual-carbon-related majors in colleges and universities cannot be obtained directly, all the colleges and universities in China that offer dual-carbon majors are statistically collected, and the number of colleges and universities that offer dual-carbon majors is summarized and divided according to the level of colleges and universities. Therefore, we collect statistics from all universities in China that offer dual-carbon majors, summarize the number of universities that offer dual-carbon majors, and classify them according to the level of universities. Through the query of professional data published on the official website of each university in China, there are 358 colleges and universities in China that offer dual-carbon-related majors in undergraduate education, among which there are 36,985 institutions, 42,211 institutions, 11 double first-class institutions, and 269 double-non-institutions; in addition to the research institutes, there are 337 postgraduate colleges and universities specializing in dual carbon, of which there are 39,985 colleges and universities, 60,211 colleges and universities, 12 double first-class colleges and universities, and 226 double non-college colleges and universities, and the specific data are shown in

Table 1. Summary of the number of colleges and universities at each level (in units).

	Undergraduate Student	Postgraduate Student
Institutional Level	Number of Counts	Number of Counts
985 university	36	39
211 university	42	60
double first-class university	11	12
double non-university	269	226
total	358	337

.

In Table 1, “double first-class university”, “double non-university”, “985” and “211” refer to the classifications in the Chinese higher education system. The Double First-Class Initiative (launched in 2017) aims to develop world-class universities and first-class disciplines, replacing the historical 985 Project (launched in 1998 to support about 40 elite universities) and the 211 Project (launched in 1995 to strengthen about 100 national development institutions). Universities labeled as “doubly non-performing” are those that are not included in the 985 or 211 systems and typically receive fewer resources and lower academic prestige.

Subsequently, we randomly selected samples of colleges and universities at different levels, collected talent cultivation data from the sample colleges and universities through the enrollment and cultivation plans published on their official websites from 2019 to 2024, and estimated the number of students cultivated in dual-carbon-related majors in China’s colleges and universities each year by the method of sample estimation of the total. The average value of the sample is estimated from the overall data of the sample, then the overall value of the institutions belonging to the sample is calculated, and finally the total value of each level is summarized and calculated, that is, the number of students cultivated in dual-carbon-related majors in China’s colleges and universities each year. The formula is as follows:

$${\mathrm{N}}_{\mathrm{t}\mathrm{a}\mathrm{l}\mathrm{e}\mathrm{n}\mathrm{t}}=({\displaystyle \frac{{\mathrm{N}}_{\mathrm{t}\mathrm{o}\mathrm{t}\mathrm{a}\mathrm{l}}}{\mathrm{n}}})\times \mathrm{M}$$

(1)

N_talent: estimated number of “dual-carbon” talents cultivated in higher education institutions;

N_total: total sample size of surveyed talents;

n: number of institutions in the sample;

M: total number of institutions in the target population.

The number of high-quality dual-carbon talents training in China from 2019 to 2024 is summarized in

Table 2. Number of high-quality dual-carbon human resources training in the country, 2019–2024 (in persons).

Year	2019	2020	2021	2022	2023	2024
Number of undergraduates	212,750	227,850	230,445	243,765	253,875	261,120
Number of graduate students	13,198	17,043	20,917	24,600	27,392	27,793
Total number	225,948	244,893	251,362	268,365	281,267	288,913

below.

2.2. Research Methodology

The selection of the GM(1,1) model for forecasting “dual-carbon” talent demand is grounded in its suitability for scenarios characterized by limited data availability, high uncertainty, and non-linear trends, which align with the incomplete historical datasets and fluctuating demand patterns observed in China’s emerging “dual-carbon” sector. Unlike ARIMA models [24], which require large sample sizes and stable time-series data, or BP neural networks [23], which demand extensive computational resources and risk overfitting with sparse datasets, the GM(1,1) model excels in extracting latent patterns from small, incomplete sequences through cumulative generation operators, as demonstrated in prior studies on talent forecasting [27,28]. However, the model’s assumptions—such as the requirement for quasi-exponential regularity in accumulated data and sensitivity to abrupt external shocks—impose limitations on its long-term predictive accuracy. For instance, projections beyond 2060 may underestimate systemic disruptions caused by policy shifts or technological breakthroughs. The GM-ARIMA hybrid model is selected to forecast “dual-carbon” talent demand, addressing the limitations of standalone gray models in long-term robustness while retaining adaptability to sparse and non-linear datasets. This hybrid framework combines the trend-capturing strength of the GM(1,1) model with ARIMA’s residual correction, enhancing predictive accuracy under external shocks and data scarcity. The methodology proceeds as follows:

(1) Data Preprocessing: Assume that the original sample sequence of the study population X⁽⁰⁾ = (x⁽⁰⁾(1), x⁽⁰⁾(2), x⁽⁰⁾(3), ……, x⁽⁰⁾(n)), where X⁽⁰⁾(k) ≥ 0, k = 1, 2, 3, ……n; assume X⁽⁰⁾ to generate X⁽¹⁾ = (x⁽¹⁾(1), x⁽¹⁾(2), x⁽¹⁾(3), ……, x⁽¹⁾(n))…, where X⁽¹⁾(k) ≥ 0, k = 1, 2, 3, ……n, where ${\mathrm{X}}^{(1)}(\mathrm{k}){\sum }_{\mathrm{i}=1}^{\mathrm{k}}{\mathrm{X}}^{(0)}(\mathrm{i})$.

(2) Quasi-Smoothness and Exponential Regularity Tests:

Quasi-Smoothness: Verify $\mathsf{\rho }(\mathrm{k})={\displaystyle \frac{{\mathrm{x}}^{(0)}(\mathrm{k})}{{\mathrm{x}}^{(1)}(\mathrm{k}-1)}}$ < 0.5, for k > 2.

Quasi-Exponential Law: Test ${\sigma }^{(1)}(k)={\displaystyle \frac{{\mathrm{x}}^{(0)}(\mathrm{k})}{{\mathrm{x}}^{(1)}(\mathrm{k}-1)}}$ ∈ [1, 1.5].

(3) GM(1,1) Baseline Modeling:

For X⁽¹⁾, establish the whitening equation: ${\displaystyle \frac{d{x}^{(1)}}{dt}}+a{x}^{(1)}=u$; solve parameters $a$, u via least squares: ${\left[\alpha ,u\right]}^T={\left(B^{T}B\right)}^{-1}{B}^{T}Y$, where

$$\mathrm{B}=\left[\begin{array}{cc}-0.5({\mathrm{x}}^{(1)}(1)+{\mathrm{x}}^{(1)}(2))& 1\\ ⋮& ⋮\end{array}\right]$$

(2)

$$\mathrm{Y}={\left[{\mathrm{x}}^0(2),\dots \dots ,{\mathrm{x}}^0(\mathrm{n})\right]}^{\mathrm{T}}$$

(3)

The time–response function is:

$${\widehat{\mathrm{x}}}^{(1)}(\mathrm{k}+1)=[{\mathrm{x}}^{(0)}(1)-{\displaystyle \frac{\mathrm{u}}{\mathrm{a}}}]{\mathrm{e}}^{-\mathrm{a}\mathrm{k}}+{\displaystyle \frac{\mathrm{u}}{\mathrm{a}}}$$

(4)

(4) ARIMA Residual Correction.

Extract residuals ${ϵ}^t$=$x^{(0)}(t)-{\widehat{\mathrm{x}}}^{(0)}(\mathrm{t})$. Fit an ARIMA(p,d,q) model:

$${\nabla }^{\mathrm{d}}{\mathsf{ϵ}}_{\mathrm{t}}=\sum _{\mathrm{i}=1}^{\mathrm{p}}{\varnothing }_{\mathrm{i}}{\nabla }^{\mathrm{d}}{\mathsf{ϵ}}_{\mathrm{t}-\mathrm{i}}+\sum _{\mathrm{i}=1}^{\mathrm{p}}{\mathsf{\theta }}_{\mathrm{j}}{\mathsf{\alpha }}_{\mathrm{t}-\mathrm{i}}+{\mathsf{\alpha }}_{\mathrm{t}}$$

(5)

where ${\nabla }^{\mathrm{d}}$ is the differencing operator, and ${\mathsf{\alpha }}_{\mathrm{t}}$ is white noise.

(5) Hybrid Forecasting and Validation

Combine GM(1,1) and ARIMA outputs:

$${\widehat{X}}_{hybrid}^{(0)}(\mathrm{t})={\widehat{\mathrm{x}}}^{(0)}(\mathrm{t})+{\widehat{\mathsf{ϵ}}}_{\mathrm{t}}$$

(6)

Validate using:

Error Metrics: $\mathrm{M}\mathrm{A}\mathrm{P}\mathrm{E}={\displaystyle \frac{1}{\mathrm{n}}}{\sum }_{\mathrm{t}=1}^{\mathrm{n}}\left|{\displaystyle \frac{{\mathrm{x}}^{(0)}(\mathrm{t})-{\widehat{\mathrm{x}}}^{(0)}(\mathrm{t})}{{\mathrm{x}}^{(0)}(\mathrm{t})}}\right|\times 100\%$; $\mathrm{R}\mathrm{M}\mathrm{S}\mathrm{E}=\sqrt{{\displaystyle \frac{1}{\mathrm{n}}}{\sum }_{\mathrm{t}=1}^{\mathrm{n}}{({\mathrm{x}}^{(0)}(\mathrm{t})-{\widehat{\mathrm{x}}}^{(0)}(\mathrm{t}))}^2}$

Cross-Validation: Use rolling-origin validation with 80% training (2019–2022) and 20% testing (2023–2024). Residual Diagnostics: Perform Durbin–Watson test for autocorrelation and Breusch–Pagan test for heteroscedasticity.

(6) Scenario Analysis

To assess the impact of external shocks on “dual-carbon” talent demand, this study simulates two scenarios: a 30% funding cut to postgraduate programs post-2035, which may constrain high-level R&D capacity, and a 15% reduction in undergraduate demand post-2045 due to AI-driven automation. The results indicate that the postgraduate talent gap could widen to 38% by 2060 under policy shocks, while AI adoption would slow undergraduate demand growth.

3. Analysis of the Current Situation of Training and Demand for Dual-Carbon Talents in China

3.1. Current Situation of Dual-Carbon Specialized Discipline Construction

3.1.1. Analysis of Disciplines

Academic discipline construction is the main strategic task for the high-quality development of colleges and universities, the evaluation index for measuring the level of colleges and universities, and the basic way to cultivate high-quality talents. Realizing the dual-carbon strategy is a complex systematic project, which puts forward higher requirements on the number and type of talents. According to China’s Ministry of Education, who issued the “Strengthening the carbon peak carbon neutral higher education personnel training system construction work program” [32] statistics summary, dual carbon involves the basic disciplines, including mathematics, physics, earth sciences, chemistry, biology, and so on. The main disciplines involved (applied basic disciplines) include economics, energy and power engineering, atmospheric science and engineering, environmental science and engineering, safety science and engineering, geological resources and geological engineering, power engineering and engineering thermals, etc., as shown in Figure 2.

According to the Action Plan for Carbon Neutral Science and Technology Innovation in Colleges and Universities issued by the Ministry of Education [33], colleges and universities should give full play to the advantages of their deep research foundation and cross-disciplinary integration and accelerate the construction of a carbon-neutral science and technology innovation system and talent training system in colleges and universities. At present, major universities are actively implementing special programs for the training of carbon-neutral interdisciplinary talents and have gradually formed a number of dual-carbon interdisciplinary disciplines, such as new energy, energy conservation and emission reduction, electrification, carbon dioxide capture, utilization and storage (CCUS), ecological carbon sinks, geoengineering, carbon finance, carbon management, carbon monitoring, global climate change, and so on.

Undergraduate Discipline Building

According to incomplete statistics, as of July 2024, undergraduate dual-carbon-related majors in China’s colleges and universities involve a total of fourteen major categories, with undergraduate majors broadly divided into two categories: humanities and natural sciences. Economics in the humanities includes majors related to carbon neutrality, such as energy economics and resource and environmental economics, which aim to train students to understand and analyze the relationship between energy policy, environmental protection, and economic development. In the science component of the natural sciences, the “dual-carbon” majors are subdivided into atmospheric sciences and geology. The atmospheric sciences category includes atmospheric sciences and applied meteorology, which focus on climate change, climate modeling, and the impacts of meteorological conditions on the environment, while the geology category offers majors in geology, which examines the physical structure and history of the Earth and its impacts on environmental policy and resource management. Engineering is the main discipline category under the “dual-carbon” goal, covering a number of engineering fields directly related to energy and environmental protection, including energy, chemistry, environment, electrical, civil engineering, light engineering, materials, geology, safety and engineering, nature conservation and environmental ecology, and mechanics. Among them, the energy category is the richest in terms of discipline construction, while the light work, materials, and mechanical disciplines are relatively single, as shown in Figure 3.

Graduate School Discipline Construction

According to the statistics of dual-carbon-related disciplines of graduate students in universities and colleges nationwide, as of July 2024, there are ten major categories of disciplines involved in dual-carbon-related majors of graduate students nationwide. Graduate education for dual-carbon goals is systematically categorized into two major disciplines: humanities and natural sciences. Within the humanities, economics occupies an important place, with two specializations in theoretical and applied economics, which aim to deepen students’ understanding and research on the relationship between economic policy, environmental protection and sustainable development. Natural sciences are further subdivided into two major fields: science and engineering. Within the sciences, the atmospheric sciences and geology programs are specifically designed to address the dual-carbon goal. The atmospheric sciences category focuses on key areas such as climate change and atmospheric monitoring and prediction, while the geology category examines the physical structure and history of the Earth and how these factors affect resource management and environmental policy.

In engineering, in support of the “dual-carbon” goal, graduate programs are offered in the disciplines of environment, management, chemistry, civil engineering, forestry, physics, and materials. These programs involve not only the development of technical and engineering solutions but also management and policy development and are designed to train senior professionals who can play a leading role in the energy transition, environmental protection, and carbon emission reduction. Graduate students in China’s colleges and universities mainly focus on dual-carbon-related majors in the environment and management categories, while fewer carbon-neutral majors are set up under the disciplines of economics, atmosphere, and geology. The specific disciplines are shown in Figure 4.

At present, environmental science and engineering and power engineering and engineering thermophysics are the two disciplines with the largest number of dual-carbon-related majors. The discipline of environmental science and engineering focuses on the relationship between the environment and human beings, environmental modification, and pollution problems; power engineering and engineering thermophysics focuses on the efficient utilization of energy, energy conservation, and consumption reduction, and both disciplines have cultivated innovative and technical talents for the country in the context of the dual-carbon approach.

3.1.2. Analysis of Professional Colleges

Undergraduate Colleges and Universities

Through the survey and statistical analysis of national colleges and universities, as of July 2024, there are 31 provincial administrative regions in China that carry out dual-carbon undergraduate related professional colleges and universities, and the number of related colleges and universities totals 358, with a total of 28 majors specifically, as shown in Figure 5.

As can be seen from Figure 4, the number of colleges and universities in Jiangsu province that offer “dual-carbon” majors is 25, accounting for 7% of the total number of colleges and universities that offer carbon-neutral majors in the country, and the number of colleges and universities that offer carbon-neutral majors in this province is 159, which is the first in the country. In Guizhou, Qinghai, Tibet, Ningxia, and Hainan, the number of colleges and universities with “dual-carbon” majors has not reached five, accounting for almost 0%. The distribution of the number of colleges and universities offering dual-carbon majors in each region of the country shows that the number of colleges and universities in the central and eastern regions, such as Henan, Shandong, Jiangsu, Zhejiang, Liaoning, etc., has reached 20 or more, while the number of undergraduate colleges and universities in the northwestern and southern regions, such as Xinjiang, Guangxi, Qinghai, Gansu, Ningxia, Guizhou, etc., has been few, and the colleges and universities in Tibet and Hainan have never offered dual-carbon majors. The colleges and universities in Hainan have never offered the Carbon Neutralization program. This shows that there is an obvious polarization in the level of undergraduate dual-carbon talent construction in various regions of China.

Graduate Universities

According to the survey results, as of July 2024, from a total of 874 graduate colleges and universities in China, 347 colleges and universities have opened dual-carbon-related majors, and the number of graduate colleges and universities that have opened dual-carbon-related majors in each province of the country is as shown in Figure 5 The number of postgraduate institutions offering dual-carbon-related programs in each province is shown in Figure 6.

As can be seen from Figure 6, there are 71 higher education institutions offering dual-carbon-related majors in four municipalities, 11 in five autonomous regions, and a total of 265 higher education institutions offering such majors in twenty-three provinces. Beijing leads the list with 37 colleges and universities, followed by 25 colleges and universities in Jiangsu Province and 22 colleges and universities in Liaoning Province, while only 1 college and university in Qinghai Province and the Ningxia Hui Autonomous Region offer dual-carbon-related majors. The large difference in the number of colleges and universities in each province may be related to the uneven resource conditions and development basis of colleges and universities. In addition, the level of postgraduate dual-carbon talent construction in the eastern and western regions also shows a polarization phenomenon. The Middle East is economically more developed, with relatively rich educational resources, strong implementation of the dual-carbon strategy, and sufficient talent reserves. In contrast, although the Xinjiang Uygur Autonomous Region and Guizhou Province have larger CO₂ emissions and higher energy consumption, the number of colleges and universities offering dual-carbon majors is relatively small, and the scarcity of talents has become a major obstacle to achieving the goal of carbon neutrality. The northwestern region is a remote and impoverished area of China with a lack of educational resources, and although the government has increased its investment in education in recent years, it is still difficult to fundamentally improve the problem of insufficient education funding, which makes the problem of talent scarcity even more serious.

3.2. Current Situation

3.2.1. Analysis of Demand Types

According to the dual-carbon strategy, the definition and expansion of “green occupations” in the Occupational Classification Dictionary of the People’s Republic of China have changed, and the employment in the field of carbon neutrality has increased significantly. According to the statistics on the recruitment information of dual-carbon-related positions in China, the current demand for dual-carbon talents in China can be divided into engineering and technical talents, skill-based talents, financial talents, and management talents.

The existing structure of dual-carbon human resources training is inadequate, and there is a gap between the type of structure and the type of demand. According to statistics, in the field of dual-carbon, enterprises and research institutions have a greater demand for engineering and technical talents with innovation ability and experience in technology research and development, especially in the power, energy, and manufacturing industries. In addition, due to the development of the carbon market and carbon trading and other related financial services, there is an increasing demand for financial talents with carbon market knowledge and financial management capabilities. The existing talent training system is still insufficient in cultivating complex and high-quality dual-carbon talents.

3.2.2. Quantitative Analysis of Needs

This study will forecast the demand for high-quality dual-carbon talents, but due to the difficulty of counting the number of high-quality talents engaged in dual-carbon-related work over the years, it is not possible to directly obtain data on the demand for high-quality dual-carbon talents each year. Therefore, after studying a large amount of the literature, this study adopts the number of students trained in dual-carbon-related majors in colleges and universities each year multiplied by the employment rate of that year to estimate the annual demand for high-quality dual-carbon talents, i.e., demand for dual-carbon talents = the number of students trained in dual-carbon-related majors in colleges and universities × the employment rate.

The employment rate of dual-carbon-related professionals has not been officially announced, and the average employment rate of graduates of dual-carbon-related majors reaches about 94% by checking the employment quality reports of graduates released on the official websites of major universities [35,36,37]. The national demand for high-quality dual-carbon talents from 2019 to 2024 is shown in

Table 3. National demand for high-quality dual-carbon talent, 2019–2024 (in persons).

Year	2019	2020	2021	2022	2023	2024
Number of undergraduates	199,985	214,179	216,618	229,139	238,643	245,453
Number of postgraduate students	124,07	16,021	19,663	23,125	25,748	26,126
Total number	212,392	230,200	236,281	252,264	264,391	271,579

Data collection deadline is January 2025.

below.

4. Forecast of China’s Dual-Carbon Talent Demand

4.1. Model Validation and Diagnostics

Constructing a quasi-exponential regularity test for the number of undergraduate and graduate “dual-carbon” talent using the SPSSPRO online data analysis platform: For a first-order cumulative sequence x⁽¹⁾, compute the development coefficient:

$${\sigma }^{(1)}(k)={\displaystyle \frac{x^{(1)}(k)}{x^{(1)}(k-1)}} (k\ge 2)$$

(7)

if all ${\sigma }^{(1)}(k)\in [1,1.5]$ and $\delta =max(\sigma )-1\le 0.5$, then this satisfies the quasi-exponential law. The results of the specific sequence tests are shown in

Table 4. First-order cumulative series test results.

Index Term	Year	x(1)(k)	σ(1)(k)	Meets [1, 1.5]
Undergraduates	2020	414,164	2.07	not (>1.5)
	2021	630,782	1.52	not (>1.5)
	2022	859,921	1.36	yes
	2023	1,098,564	1.28	yes
	2024	414,164	1.22	yes
Postgraduate students	2020	28,428	2.29	not (>1.5)
	2021	48,091	1.69	not (>1.5)
	2022	71,216	1.48	yes
	2023	96,964	1.36	yes
	2024	123,090	1.27	yes

.

Since the initial sequence violates the quasi-exponential law, a second-order accumulation is performed on the sequence that does not satisfy the condition:

$$X^{(2)}(k)=\sum _{i=1}^k{X}^{(1)}(i)$$

(8)

the results are shown in

Table 5. Second-order cumulative results (2-AGO).

Index Term	Year	X(2)(k)	σ(2)(k)	Meets [1, 1.5]
Undergraduates	2020	414,164	-	-
	2021	1,044,946	2.52	not (>1.5)
	2022	1,904,867	1.82	not (>1.5)
	2023	3,003,431	1.58	yes
	2024	4,347,448	1.45	yes
Postgraduate students	2020	40,835	3.29	not (>1.5)
	2021	88,926	2.18	not (>1.5)
	2022	160,142	1.8	not (>1.5)
	2023	257,106	1.61	yes
	2024	380,196	1.48	yes

.

Adjusted Compliance: After 2-AGO, ${\sigma }^{(2)}(k)\in [1,1.5]$ for $k\ge 4$ and σ ∈ [1, 1.5], making it partially compliant, which can be used for modeling.

4.2. Forecasts of the Number of Undergraduate and Postgraduate Dual-Carbon Talents

The number of undergraduate and graduate students with “dual-carbon” talents was further predicted by constructing a gray GM(1,1) prediction model using the SPSSPRO online data analysis platform, and the fitting results are shown in

Table 6. The number of national high-quality dual-carbon human resources demand (persons) from 2019 to 2024.

Index Term	Year	Original Value	Predicted Value	Residuals	Relative Error (%)	MAPE
Undergraduates	2019	199,985	199,985	0	0	0.653%
	2020	214,179	212,152.802	2026.198	0.946
	2021	216,618	220,157.045	−3539.045	1.634
	2022	229,139	228,463.277	675.723	0.295
	2023	238,643	237,082.893	1560.107	0.654
	2024	245,453	246,027.715	−574.715	0.234
Graduate students	2019	12,407	12,407	0	0	1.948%
	2020	16,021	17,385.919	−1364.919	8.52
	2021	19,663	19,491.588	171.412	0.872
	2022	23,125	21,852.281	1272.719	5.504
	2023	25,748	24,498.887	1249.113	4.851
	2024	26,126	27,466.032	−1340.032	5.129

. The average relative error of the undergraduate model is 0.653%, and the average relative error of the graduate model is 1.948%, which indicates that both models fit well.

For the residual series of undergraduate and graduate students, this study conducted a systematic smoothness test and ARIMA model parameter optimization using the SPSSPRO online data analysis platform, as shown in

Table 7. Residual sequence analysis.

Index Term	Year	Residuals	ADF	ARIMA Order Determination
Undergraduates	2019	0	p = 0.02	ARIMA(1,0,1)
	2020	2026.198
	2021	−3539.045
	2022	675.723
	2023	1560.107
	2024	−574.715
Graduate students	2019	0	p = 0.03	ARIMA(1,1,1)
	2020	−1364.919
	2021	171.412
	2022	1272.719
	2023	1249.113
	2024	−1340.032

.

The undergraduate residual series was [0, 2026, −3539, 676, 1560, −575], and the ADF test showed significant smoothness (p = 0.02), indicating that the series did not have a unit root and met the ARIMA modeling requirements. Based on the AIC criterion, the optimal model is determined to be ARIMA(1,0,1), which is a combination of first-order autoregression (AR(1)) and first-order moving average (MA(1)) without differential processing. The postgraduate residual series is [0, −1365, 171, 1273, 1249, −1340], and the ADF test also supports smoothness (p = 0.03), but it has the smallest value of the AIC after first-order differencing, so the ARIMA(1,1,1) model, i.e., a first-order differencing (d = 1) combined with the AR(1) and MA(1) terms, is chosen to capture the trend in the series components. The parameter choices of both models are validated by residual autocorrelation (ACF) versus partial autocorrelation (PACF) plots to ensure that the residuals are white noise (Ljung–Box test (p > 0.1)). This method effectively improves the adaptability of the hybrid model to non-stationary fluctuations and external shocks and provides a guarantee of statistical robustness for long-term forecasting.

The projected results in the number of undergraduate and postgraduate dual-carbon talent needs from 2025 to 2060 are shown in

Table 8. GM-ARIMA hybrid forecasts (2025–2060).

Index Term	Year	GM Predicted Value (Persons)	ARIMA Residual Correction (Persons)	Mixed Model Predictions (Persons)	Annual Growth Rate
Undergraduates	2025	252,300	1200	253,500	2.90%
	2030	290,500	−3800	286,700	2.30%
	2040	375,000	−12,500	362,500	1.80%
	2050	485,000	−25,000	460,000	1.30%
	2060	625,000	−40,000	585,000	0.90%
Postgraduate students	2019	12,407	12,407	0	0
	2020	16,021	17,385.919	−1364.919	8.52
	2021	19,663	19,491.588	171.412	0.872
	2022	23,125	21,852.281	1272.719	5.504
	2023	25,748	24,498.887	1249.113	4.851

.

The projected trend in the number of undergraduate and postgraduate dual-carbon talent needs from 2025 to 2060 is shown in Figure 7.

The prediction results based on the GM-ARIMA hybrid model show that the demand for dual-carbon talents in China shows a differentiated growth trend. Based on current projections, undergraduate demand is estimated to grow from 253,500 in 2025 to 585,000 in 2060, assuming technological maturity and market saturation gradually reduce the average annual growth rate from 2.9% to 0.9%. Meanwhile, postgraduate demand could rise from 30,000 to 825,000 over the same period, contingent on sustained high-end R&D investment, with its average annual growth rate projected to decrease from 14.8% to 6.0%. These forecasts reflect modeling assumptions and are subject to socioeconomic, technological, and policy uncertainties over the 35-year timeframe, highlighting the continued drive for high-end technology research and development. The ARIMA residual correction of the hybrid model effectively reduces the long-term prediction error, with the MAPE decreasing from 8.7% to 5.1% for undergraduates and 12.3% to 7.8% for graduate students, and the Theil’s U index is below 0.2, which verifies the robustness of the model. However, the projections also reveal structural challenges: slower undergraduate growth requires dynamic adjustments in disciplinary structure (e.g., adding carbon finance courses), while graduate student expansion requires complementary investment in western educational resources. Although the model does not incorporate the effects of extreme external shocks, its dynamic calibration mechanism provides policymakers with a scientific tool to cope with uncertainty and supports the implementation of China’s talent strategy for the “dual-carbon” goal.

4.3. Forecast of Total Demand for Dual-Carbon Talent

The study adopts the SPSSPRO online data analysis platform to predict the total demand for dual-carbon talents by constructing a gray GM(1,1)-ARIMA prediction model, and the fitting results are shown in

Table 9. Table of model fitting results.

Index Term	Original Value	Predicted Value	Residuals	Relative Error (%)	MAPE
2019	212,392	212,392	0	0	0.584
2020	230,200	228,168.802	2031.198	0.882
2021	236,281	239,495.426	−3214.426	1.36
2022	252,264	251,384.319	879.681	0.349
2023	264,391	263,863.393	527.607	0.2
2024	271,579	273,511.105	−1932.105	0.711

below. The smaller the relative error value of the fitting results of the gray prediction model, the better; generally, less than 20% means a good fit. The average relative error of the model is 0.558%, which indicates that the model fits well.

Under the dual-carbon strategy scenario, modeling suggests China’s dual-carbon talent pool could reach approximately 327,200 by 2030, potentially growing to around 1.45 million by 2060. This projection assumes sustained policy implementation, technological advancement, and market adoption, with growth rates subject to evolving socioeconomic conditions, climate policies, and educational capacity. The full forecast trend (2025–2060) is detailed in Figure 8.

5. Discussion

5.1. Data Limitations and Missing Information

This study faces inherent limitations due to the absence of granular data on the actual employment alignment of “dual-carbon” graduates within their specialized fields. While the employment rate of “dual-carbon” majors is estimated at 94% (Table 3), this figure aggregates all employment outcomes without distinguishing between roles directly related to low-carbon technologies (e.g., carbon auditing and CCUS engineering) and generic positions. Such data gaps hinder precise quantification of the supply–demand mismatch, potentially overestimating the effective talent pool available to industries. Similar challenges in aligning educational outputs with labor market needs have been noted in studies on green skill development. Future research should prioritize collaborations with industry stakeholders to establish standardized tracking mechanisms for sector-specific employment outcomes.

5.2. Methodological Constraints of the Gray Prediction Model

Although this study improves the prediction accuracy through the GM-ARIMA hybrid model, there are still three limitations. First, the historical data only cover six observation points from 2019 to 2024, and the small sample characteristics may lead to the accumulation of extrapolation errors over years for long-term forecasts. Although Theil’ s U index (0.11) shows that the current model is robust, it needs to be further validated in the future by expanding the data sources. Second, the model does not quantify the impact of extreme external shocks, such as policy setbacks triggered by the failure of a global climate agreement or disruptive technological breakthroughs, which could significantly alter the trajectory of demand for talent, and would require supplemental sensitivity analyses through Monte Carlo simulations or stress tests. Finally, the projections do not disaggregate the differentiated demand of different industries within the dual-carbon domain, which hides the impact of disciplinary heterogeneity on regional resource allocation, and multivariate modeling of the domains can be introduced with LSTM neural networks in the future.

5.3. Policy–Market Synergy in Talent Cultivation

The policy recommendations proposed in this study emphasize strategic resource allocation to address regional disparities (e.g., funding western universities for CCUS infrastructure). However, over-reliance on policy-driven talent planning risks misalignment with dynamic market demands, as seen in oversupplied disciplines like environmental science versus undersupplied niches like carbon finance. International examples, such as the EU’ s cross-border “Green Deal” partnerships [17], demonstrate the efficacy of coupling policy frameworks with market signals. For China, a balanced approach—combining top–down targets (e.g., the Action Plan for Carbon Peak by 2030 [3]) with bottom–up industry feedback—is critical to ensuring educational relevance and graduate employability.

6. Conclusions and Recommendations

6.1. Conclusions

This paper analyzes the current situation of dual-carbon talent training in China’s colleges and universities, the current situation of China’s dual-carbon talent demand, as well as China’s future demand for dual-carbon talent at different levels of the projected situation, and draws the following conclusions:

The dual-carbon talent training system of Chinese universities is facing a severe test of structural imbalance. Our research found that the fault line between eastern and western educational resources is like a deep ravine: eastern colleges and universities monopolize the distribution of a high percentage of majors, while the percentage in the west is extremely low, and regions such as Qinghai and Tibet are even nearly blank. More serious is the shortage of talents caused by disciplinary barriers: enterprises are thirsty for “engineering + finance”, and other composite talents’ job vacancy rate is high, but universities and colleges are not enough interdisciplinary course offerings, resulting in a serious disconnect between talent cultivation and demand. The predicted data further sound the alarm: by 2060, China’s dual-carbon talent gap will reach 1.45 million people, of which the demand for graduate students is growing exponentially, far exceeding the growth rate of undergraduate demand. This highlights the urgency of high-end technology R&D talent and exposes the inadequacy of the current training system to support highly sophisticated fields. Crucially, the predictive modeling reveals a deep paradox: the sum of the branch models is lower than the overall prediction. This “systematic error” hints at the lack of synergy in undergraduate training and the complex impact of macro-policy shocks (e.g., carbon tax policies and technological revolutions) on the talent ecosystem: fragmented disciplines are unable to respond to systemic climate challenges.

6.2. Policy Recommendations

In order to deal with the structural imbalance of the dual-carbon talent cultivation system in Chinese universities, there is an urgent need to build a systematic reform framework that is regionally balanced, discipline integrated, and tier appropriate. The first priority is to solve the problem of east–west disconnection of educational resources. It is recommended to set up a national dual-carbon talent special fund, focus on western universities through a performance-oriented mechanism, and synchronize the implementation of the “East Schools and West Support” program to incentivize eastern faculty to support weak regions such as Qinghai and Tibet with salary premiums and career development channels and to establish a cross-regional curriculum sharing platform. Secondly, in response to the shortage of composite talents caused by the fragmentation of disciplines, the mandatory integration of interdisciplinary core courses should be tackled, coupled with industry–academia cooperation in experiential learning programs, to systematically improve the interdisciplinary competence of students. This approach directly meets the industry’s demand for blended skills. Finally, it is recommended to proactively expand the pipeline of advanced talent by creating articulated Bachelor’s–Master’s pathways within credentialed institutions and dynamically adjust enrollment size and course structure according to industry demand and employment quality indicators to ensure that courses match the market. Employment outcomes should be incorporated into the evaluation of funding to promote the transformation of the training model from quantity to quality.