Analysis of Carbon Emission Characteristics and Influencing Factors of Cement Industry in Hebei Province

Abstract

As a crucial provider of building materials, the cement industry occupies a central position in developing Hebei Province’s construction sector while presenting considerable challenges to achieving regional carbon neutrality targets. This paper establishes a carbon emission accounting model for cement production, quantifies the carbon emission trajectory of Hebei’s cement industry from 2005 to 2023, applies the STIRPAT (Stochastic Impacts by Regression on Population, Affluence, and Technology) model to analyze the influencing factors of carbon emissions, and predicts future emissions from 2024 to 2035 using scenario analysis methods. The results indicate that the overall carbon emissions from the cement industry in Hebei Province exhibited fluctuating trends between 2005 and 2023, primarily driven by carbonate decomposition and fossil fuel combustion during the clinker calcination stage. Population size, GDP per capita, urbanization rate, industrial structure, energy consumption structure, cement consumption structure, and cement production were identified as positive contributors to carbon emissions. In contrast, energy intensity was found to have a mitigating effect. The prediction results show that the industry reached its carbon emissions peak at 70.29 million tCO₂e in 2020. Under the enhanced low-carbon scenario, emissions are expected to decline by 20.9% relative to the baseline scenario, reaching 34.95 million tCO₂e by 2035. Deep emission reductions should be achieved through technological upgrading and policy guidance to support the low-carbon transformation of Hebei’s cement industry and promote sustainable urbanization.

1. Introduction

As a fundamental industry, cement plays a key role in national infrastructure construction and urbanization. However, its high energy consumption and carbon emissions have also made it a major focus of global climate change mitigation strategies. China is the world’s largest producer and consumer of cement [1,2]. In 2023, China produced 2.02 billion tons of cement, accounting for about 49% of the global output [3]. Carbon emissions from the cement industry constituted about 11% of China’s total carbon emissions, making it the third-largest carbon-emitting sector after the power and steel industries [4]. Against an accelerating global climate governance process, the cement industry faces increasingly stringent carbon emission regulations. The European Union formally included cement and related building materials products (such as ready-mixed concrete and prefabricated components) in the Carbon Border Adjustment Mechanism (CBAM) in 2023, which requires accurate accounting of product carbon emissions and the payment of carbon tariffs [5,6]. This initiative reshaped the global trade rules and forced China’s cement industry to accelerate low-carbon transformation. Meanwhile, China is steadily enhancing its domestic carbon market mechanism. In 2025, the Ministry of Ecology and Environment issued the “National Carbon Emission Trading Market Coverage of Iron and Steel, Cement, and Aluminum Smelting Industries Work Plan” [7], officially incorporating the cement sector into the national emissions trading system. Therefore, under the combined impetus of the dual-carbon strategy and evolving domestic and international policies, accelerating carbon emission reductions in the cement industry is a crucial step toward achieving national carbon neutrality targets and promoting sustainable, low-carbon growth in the construction sector.

Current research on carbon emissions in the cement industry primarily focuses on macro-level trend forecasting, micro-level product carbon footprint assessment, and carbon emission driver analysis. At the macro level, researchers commonly employ scenario simulation methods to construct carbon emission forecasting frameworks. He et al. [8] utilized a multi-factor fitting analysis model to predict the carbon peaking trajectory of China’s cement industry. Cheng et al. [9] adopted a bottom-up carbon measurement model to project cement carbon emissions from 2020 to 2050, based on the current development status of emerging economies, thereby providing a theoretical foundation for international emission reduction policy design. While such macro-level studies offer insights into industry-wide trends, they often overlook regional disparities in emissions arising from differences in industrial structure, economic development levels, and policy environments across provinces. At the micro-product level, Shang et al. [10] applied the life cycle assessment (LCA) method to compare the carbon emissions of sulphoaluminate cement in China and abroad, highlighting the efficiency gap and identifying key emission reduction pathways. Kristine et al. [11] compared the carbon footprints of Portland cement and enzyme-repairing cement, emphasizing the role of material innovation as a critical factor in reducing emissions. Such studies contribute to improving product-level carbon efficiency at the product level but lack linkage analysis with regional industrial structure and policy environment. In terms of emission driver analysis, research exhibits increasing methodological diversity. Liang et al. [12] used the gray correlation analysis method and the STIRPAT (Stochastic Impacts by Regression on Population, Affluence, and Technology) model to demonstrate significant regional flexibility in identifying key drivers in Hebei province. Du et al. [13] employed the Logarithmic Mean Divisia Index (LMDI) method to investigate CO₂ emission determinants in China’s cement sector from supply and demand perspectives. Using the IDA model, Shan et al. [14] concluded that economic growth is the primary driver of increasing emissions in the cement industry. In contrast, emission intensity and technological efficiency serve as key mitigating factors.

Although existing studies have examined the carbon emissions of the cement industry from many perspectives, the following shortcomings persist: (1) Research at the regional level is constrained, predominantly concentrating on national or urban analyses, particularly deficient in a systematic examination of provinces with a significant share of cement manufacturing, such as Hebei. (2) The study on driving forces and anticipated scenarios has not been effectively integrated. Current single-model research struggles to address the interacting effects of intricate policy scenarios and fails to offer quantitative decision support for regional policy formulation. Therefore, there is an urgent need for comprehensive provincial-level research that combines influencing factor analysis with scenario forecasting to enrich the current academic discourse.

In response to the above deficiencies, this study takes the cement industry in Hebei Province as the research object, calculates the carbon emissions of cement from 2005 to 2023, employs a combination of the STIRPAT model and scenario analysis to identify the main influencing factors of carbon emissions, and constructs the carbon emission trends of the cement industry in Hebei Province from 2024 to 2035 under three scenarios: the baseline scenario, the low-carbon scenario, and the enhanced low-carbon scenario. This analysis not only elucidates the fundamental mechanisms of carbon emissions within the provincial cement sector but also evaluates the potential for emission reductions under varying intensities of policy interventions, thereby serving as a reference for attaining regional carbon reduction objectives and facilitating the industry’s low-carbon transition.

2. Methods and Data Sources

2.1. Cement Industry Carbon Emission Accounting Method

This study calculates carbon emissions across the entire cement production process, including mining raw materials (such as limestone and gypsum), transporting raw materials, process-related emissions, fuel combustion during production, and indirect emissions associated with purchased electricity consumption. Carbon emissions from the cement industry are estimated based on the “Guidelines for Accounting and Reporting of Enterprise Greenhouse Gas Emissions—Cement Industry (CETS-AG-02.01-V01-2024)” [15] (hereafter referred to as “the Guide”) and the emission factor methodology recommended by the Intergovernmental Panel on Climate Change (IPCC) [16]. The carbon emission calculation formula is as follows:

$$\mathrm{E}={\mathrm{E}}_{\mathrm{m}}+{\mathrm{E}}_{\mathrm{t}}+{\mathrm{E}}_{\mathrm{c}}+{\mathrm{E}}_{\mathrm{p}}+{\mathrm{E}}_{\mathrm{e}}$$

(1)

where E is the carbon emission of the cement industry, tCO₂e; E_m is the carbon emission of the raw material mining stage, tCO₂e; E_t is the carbon emission of the raw material transportation stage, tCO₂e; E_c is the carbon emission of the fuel combustion process, tCO₂e; E_p is the carbon emission of the clinker production process, tCO₂e; and E_e is the carbon emission of electricity consumption, tCO₂e.

The raw material mining stage primarily pertains to the carbon emissions from extracting raw materials, such as limestone, clay, sandstone, and gypsum. The calculation formula is as follows:

$${\mathrm{E}}_{\mathrm{m}}={\sum }_{\mathrm{i}=1}^{\mathrm{n}}{\mathrm{O}}_{\mathrm{i}}\times {\mathrm{M}}_{\mathrm{i}}$$

(2)

where O_i is the carbon emission factor of the ith raw material, kgCO₂e/t; M_i is the consumption of the ith raw material, t; and i is the type of raw material used. The carbon emission factors of the main raw materials refer to the Chinese Life Cycle Database (CLCD), the “Standard for building carbon emission calculation (GB/T51366-2019)”, [17] and research by various scholars [18,19].

The calculation formula for the raw material transportation stage is as follows:

$${\mathrm{E}}_{\mathrm{t}}={\sum }_{\mathrm{i}=1}^{\mathrm{n}}{\mathrm{M}}_{\mathrm{i}}\times {\mathrm{D}}_{\mathrm{i}}\times {\mathrm{T}}_{\mathrm{i}}$$

(3)

where D_i is the average transportation distance of the main raw material, km; and T_i is the carbon emission factor of the transportation distance per unit weight under the transportation mode of the ith main raw material, kgCO₂e/(t·km). The transportation of raw materials is mainly by road and railway, and the carbon emission factor in the transportation stage adopts the carbon emission factor for the transportation of building materials provided in the “Standard for building carbon emission calculation (GB/T51366-2019)”.

The process emissions are the CO₂ emissions from the decomposition of carbonates corresponding to clinker and are calculated as follows:

$${\mathrm{E}}_{\mathrm{p}}={\mathrm{Q}}_{\mathrm{c}\mathrm{l}}\times {\mathrm{E}\mathrm{F}}_{\mathrm{c}\mathrm{k}}$$

(4)

where Q_cl is the clinker yield for clinker production, kg; and EF_ck is the emission factor of the clinker production process, tCO₂e/t. For EF_ck, 0.535 tCO₂e/t is taken from the Guide.

The stage of fossil fuel combustion refers to the carbon emissions generated by the combustion of fossil fuels in the cement kiln. Coal is the primary fuel used in the manufacturing of cement [20] and is calculated as follows:

$${\mathrm{E}}_{\mathrm{c}}={\mathrm{Q}}_{\mathrm{c}\mathrm{l}}\times \mathrm{F}\mathrm{C}\times \mathrm{N}\mathrm{V}\mathrm{C}\times \mathrm{C}\mathrm{C}\times \mathrm{O}\mathrm{F}\times {\displaystyle \frac{44}{12}}$$

(5)

where FC is the fossil fuel consumption for cement clinker production, kg; NCV is the fossil fuel received base low-level heat generation, GJ/t; CC is the carbon content per unit calorific value of fossil fuels, tC/GJ; OF is the carbon oxidation rate of fossil fuels, %; and 44/12 is the ratio of carbon dioxide to the relative molecular mass of carbon. NCV, CC, and OF are all derived from the Guide.

Electricity is consumed throughout all stages of cement production and is calculated as follows:

$${\mathrm{E}}_{\mathrm{e}}=({\mathrm{A}\mathrm{D}}_{\mathrm{c}\mathrm{k}}-{\mathrm{A}\mathrm{D}}_{\mathrm{w}\mathrm{h}})\times \mathrm{f}\times {\mathrm{E}\mathrm{F}}_{\mathrm{e}}+{\mathrm{A}\mathrm{D}}_{\mathrm{c}\mathrm{g}}\times {\mathrm{E}\mathrm{F}}_{\mathrm{e}}$$

(6)

where AD_ck is clinker power consumption, kWh; AD_wh is waste heat power generation, kWh; AD_cg is cement grinding power consumption, kWh; f is the clinker incorporation coefficient, 65% [21]; and EF_e is the electric power emission factor, kgCO₂e/kWh. EF_e adopts the national average carbon footprint factor of electric power for the year 2023, 0.6205 kgCO₂e/kWh, which is the latest released by the Ministry of Ecology and Environment. According to “The norm of energy consumption per unit product of cement (GB16780-2021)”, the comprehensive power consumption of clinker is 61 kWh/t, the comprehensive power consumption of cement is 34 kWh/t, and the waste heat power generation is 35 kWh/t [22].

2.2. STIRPAT Model

2.2.1. Model Construction

With increasing complexity in research subjects and environmental contexts, the traditional IPAT model can only analyze three influencing factors: population size, affluence, and technological level. It also cannot deal with the nonlinear relationship between the variables in reality, which makes it challenging to meet the current research demands. York and Dietz extended and refined the IPAT framework to overcome these limitations by developing a stochastic version known as the STIRPAT model [23]. The specific form of the model is expressed as follows:

$$\mathrm{I}=\mathrm{a}{\mathrm{P}}^{\mathrm{b}}{\mathrm{A}}^{\mathrm{c}}{\mathrm{T}}^{\mathrm{d}}\mathrm{e}$$

(7)

where I is the environmental pressure, P is the population size, A is the affluence degree, T is the technical level, a is the constant of proportionality term of the model, b, c, and d are, respectively, the elasticity coefficients of the variables, and e is the model error term.

The STIRPAT model offers substantial flexibility, allowing for the inclusion and adjustment of influencing indicators according to specific research contexts. It accommodates multilevel explanatory variables and effectively captures complex nonlinear relationships through logarithmic linearization. Given its widespread application in studies on environmental drivers, the STIRPAT model is adopted in this study to construct a carbon emission driving framework for the cement industry in Hebei Province.

2.2.2. Variable Selection

Consider that multiple dimensions influence carbon emissions in the cement industry using only three variables—population, economy, and technology—is insufficient to capture the actual situation’s complexity fully. To enhance the explanatory power and policy relevance of the model, this study incorporates extended variables across four dimensions:

• Population factors

Population size directly affects construction demand and cement consumption, making it a fundamental driver of carbon emissions. Urbanization promotes population concentration, increasing the need for housing and infrastructure, thereby stimulating construction activity and cement demand. Accordingly, the permanent resident population and urbanization rate of Hebei Province are selected as key indicators at the population level.

2.

Economic factors

To capture the impact of economic development on carbon emissions, this study selects per capita GDP and industrial structure as key indicators. Per capita GDP reflects regional economic development; economic growth is typically accompanied by increased infrastructure investment and cement demand, driving carbon emissions. Regarding industrial structure, cement is primarily used in the secondary sector. A higher share of the secondary industry indicates a more significant presence of energy-intensive sectors, further contributing to increased carbon emissions. Therefore, the proportion of the secondary sector is adopted as a proxy for industrial structure.

3.

Technological factors

Technological advancement plays a crucial role in reducing carbon emissions by improving energy efficiency. Energy intensity is a commonly used indicator to assess energy utilization efficiency and technological level across countries or regions. Cement kilns are the primary equipment in cement production, and their high energy consumption results in a heavy reliance on fossil fuels, particularly coal. In 2023, coal accounted for 70.2% of the total energy consumption in Hebei Province, exceeding the national average [24]. Thus, the proportion of coal consumption is a representative indicator of the energy consumption structure.

4.

Industry factors

Cement production is a direct carbon emission source and a key variable controlling total emissions. The cement consumption structure, represented by the clinker ratio, reflects the share of clinker in total cement production. A lower clinker ratio corresponds to reduced carbon emissions during the production process. It indicates a higher share of low-carbon cement products, effectively contributing to the cement industry’s emission reduction. This study uses the clinker-to-cement ratio to reveal the structure of cement consumption.

Based on the eight factors identified above, the following extended STIRPAT model is developed to analyze the drivers of carbon emissions:

$$\mathrm{l}\mathrm{n}\mathrm{I}=\mathrm{l}\mathrm{n}\mathrm{a}+\mathrm{b}\mathrm{l}\mathrm{n}\mathrm{P}+\mathrm{c}\mathrm{l}\mathrm{n}\mathrm{A}+\mathrm{d}\mathrm{l}\mathrm{n}\mathrm{U}+\mathrm{e}\mathrm{l}\mathrm{n}\mathrm{I}\mathrm{S}+\mathrm{f}\mathrm{l}\mathrm{n}\mathrm{T}+\mathrm{g}\mathrm{l}\mathrm{n}\mathrm{E}\mathrm{S}+\mathrm{h}\mathrm{l}\mathrm{n}\mathrm{C}\mathrm{S}+\mathrm{i}\mathrm{l}\mathrm{n}\mathrm{B}+\mathrm{l}\mathrm{n}\mathrm{e}\mathrm{t}$$

(8)

where lna is the constant term, b, c, d, e, f, g, h, and i are the elastic coefficients of each exponential term, respectively, and et represents the error term. The variables are described in

Table 1. Description of variables in STIRPAT model.

Variable	Symbol	Unit	Explanation
Carbon emission	I	Ten thousand tons	Hebei province cement industry carbon emissions
Population size	p	Ten thousand people	Total population of Hebei Province
GDP per capita	A	Yuan/person	Regional GDP/Total population
Urbanization rate	U	%	Urban population/Total population
Industrial structure	IS	%	Output value of the secondary industry/Regional GDP
Energy intensity	T	Tons tce/ ten thousand yuan	Total energy consumption/Regional GDP
Energy consumption structure	ES	%	Coal energy usage/ Total energy usage
Cement consumption structure	CS	%	Clinker production/ Cement production
Cement production	B	Ten thousand tons	Cement production of Hebei Province

.

2.3. Scenario Analysis Method

2.3.1. Scenario Setting

This study integrates relevant policy objectives and the current energy utilization status in Hebei Province. Taking 2023 as the base year, the forecast period spans from 2024 to 2035. Three scenarios—the baseline scenario, the low-carbon scenario, and the enhanced low-carbon scenario—are established to project and analyze future carbon emissions from the cement industry in Hebei Province.

The baseline scenario reflects the continuation of Hebei Province’s current development trajectory. All influencing factors are assumed to follow their historical average trends without implementing additional or high-intensity carbon reduction policies. It serves as a reference case to evaluate the impact of future policy interventions.

The low-carbon scenario incorporates green and low-carbon targets outlined in key national and provincial policy documents, such as Hebei’s “14th Five-Year Plan”, the “Special Action Plan for Energy Conservation and Carbon Reduction in the Cement Industry”, and the “14th Five-Year Plan” for green and low-carbon development of the cement industry [25]. Under this scenario, improvements in energy efficiency and adjustments in industrial structure are emphasized to promote moderate carbon reductions in the cement industry.

The enhanced low-carbon scenario is based on the low-carbon scenario and adopts more stringent carbon reduction measures. These include mandatory adoption of advanced low-carbon production technologies, comprehensive retrofitting for energy savings and emissions reduction, substantial energy mix optimization, expanded use of clean energy sources, and stricter controls on energy consumption. The goal is to minimize carbon emissions from Hebei’s cement industry to the greatest possible extent.

2.3.2. Parameter Setting

• Population size

From 2005 to 2023, the population of Hebei Province transitioned from growth to negative growth. Drawing on population projection studies by Chen et al. [26] and Li et al. [27] and referencing estimates from both the United Nations and national Chinese sources, the average annual population growth rate under the baseline scenario is assumed to be −0.35% from 2023 to 2030 and −0.30% from 2030 to 2035. In the low-carbon and enhanced low-carbon scenarios, the rate of population decline is assumed to decelerate slightly, reflecting the potential impact of adjustments in population policy and shifts in fertility intentions.

2.

Per capita GDP

This study adopts these values as the basis for setting economic growth assumptions based on the target of an average annual GDP growth rate of 6% outlined in Hebei Province’s “14th Five-Year Plan” and the forecast by the Development Research Center of the State Council projecting a national average annual economic growth rate of 5% for the period from 2026 to 2030.

3.

Urbanization rate

The parameter setting of the urbanization rate is based on the “Hebei Statistical Yearbook”, which states that the urbanization rate in 2023 is 62.77%; the “14th Five-Year Plan” mentions that the urbanization rate of the permanent resident population will reach over 65% by 2025, and the “Population Development Plan of Hebei Province (2018–2035)” states that the target for 2035 is around 70%.

4.

Industrial structure

In recent years, the share of the secondary industry in Hebei Province has steadily declined, while the proportion of the tertiary sector has gradually increased. Drawing on the industrial structure adjustment targets outlined in the “14th Five-Year Plan” of Hebei Province and projections from the report “China 2030: Building a Modern, Harmonious, and Creative Society”, this study sets the average annual rate of change for the secondary industry’s share under each scenario. In the enhanced low-carbon scenario, a more rapid industrial structure adjustment is assumed to reflect the intensified regulation of energy-intensive sectors driven by green transformation pressures.

5.

Energy intensity

Drawing on “China’s Energy Transition” white paper released by the State Council in 2024 and the energy targets outlined in the “14th Five-Year Plan” of Hebei Province, and taking into account the historical downward trend in energy intensity, the baseline scenario assumes an average annual decline of 3.0% in energy consumption per unit of GDP. In the low-carbon and enhanced low-carbon scenarios, this reduction is further intensified through technological innovation and improvements in energy efficiency.

6.

Energy consumption structure

The proportion of coal consumption in Hebei Province declined from 88.7% in 2013 to 70.2% in 2023. Based on the “BP World Energy Outlook”, which projects China’s coal share to fall to 35% by 2040 [28], the baseline scenario adopts an average annual decrease of 2.35%. Structural optimization is accelerated in the low-carbon and enhanced low-carbon scenarios by substituting coal with cleaner energy sources.

7.

Cement production and cement consumption structure

Based on historical development trends, “A Study on the Carbon Neutrality Pathways of China’s Cement Industry”, and “Energy Conservation and Carbon Reduction Action Plan for 2024–2025”, cement output in China has gradually declined. According to the IEA’s “Technology Roadmap: Low-Carbon Transition in the Cement Industry”, the clinker ratio in cement is projected to decrease to 0.64 by 2030, 0.63 by 2040, and 0.60 by 2050 [29]. Accordingly, this study’s parameter settings for cement output and consumption structure align with these projections.

The specific settings of each parameter for each scenario are shown in

Table 2. Scenario parameter rate of change settings.

Parameter Category	Scene	2024–2025	2026–2030	2030–2035
Population size	Baseline scenario	−0.35%	−0.35%	−0.30%
	Low-carbon scenario	−0.30%	−0.25%	−0.20%
	Enhanced low-carbon scenario	−0.25%	−0.20%	−0.15%
GDP per capita	Baseline scenario	6.00%	5.50%	5.00%
	Low-carbon scenario	5.50%	5.00%	4.60%
	Enhanced low-carbon scenario	5.00%	4.80%	4.40%
Urbanization rate	Baseline scenario	1.76%	0.96%	0.56%
	Low-carbon scenario	1.26%	0.90%	0.55%
	Enhanced low-carbon scenario	0.90%	0.85%	0.50%
Industrial structure	Baseline scenario	−1.13%	−1.13%	−1.13%
	Low-carbon scenario	−1.20%	−1.50%	−1.80%
	Enhanced low-carbon scenario	−1.25%	−1.60%	−1.90%
Energy intensity	Baseline scenario	−3.00%	−3.00%	−3.00%
	Low-carbon scenario	−3.30%	−3.20%	−3.10%
	Enhanced low-carbon scenario	−3.50%	−3.40%	−3.30%
Energy consumption structure	Baseline scenario	−2.35%	−2.35%	−2.35%
	Low-carbon scenario	−2.50%	−2.80%	−3.00%
	Enhanced low-carbon scenario	−2.80%	−3.00%	−3.50%
Cement consumption structure	Baseline scenario	−2.50%	−2.70%	−2.90%
	Low-carbon scenario	−2.80%	−2.90%	−3.10%
	Enhanced low-carbon scenario	−3.00%	−3.10%	−3.20%
Cement production	Baseline scenario	−0.26%	−0.16%	−0.16%
	Low-carbon scenario	−0.24%	−0.24%	−0.24%
	Enhanced low-carbon scenario	−0.29%	−0.29%	−0.29%

.

2.4. Data Sources

Data on raw material and energy consumption at each stage of cement production were obtained from the National Bureau of Statistics of China, China Energy Statistical Yearbook, China Cement Association, Digital Cement Network, and relevant literature studies [30,31,32,33]. In addition, the data for the influencing factors are from the China Statistical Yearbook, the Hebei Statistical Yearbook, the China Energy Statistical Yearbook, and the China Emission Accounts and Datasets (CEADs). The data for the scenario analysis come from several sources, including plans and reports, such as “The Outline of the 14th Five-Year Plan for National Economic and Social Development of Hebei Province and the Long-Range Objectives Through the Year 2035”, “Population Development Plan of Hebei Province (2018–2035)”, “China 2030: Building a Modern, Harmonious, and Creative Society”, and “Energy Conservation and Carbon Reduction Action Plan for 2024–2025”, along with other related documents.

3. Results and Analysis

3.1. Carbon Emissions from the Cement Industry in Hebei Province

Figure 1 shows that the overall carbon emissions of the cement industry in Hebei Province fluctuated from 2005 to 2023, decreased significantly after reaching a peak in 2011, and then gradually increased again. This variation reflects the impact of rapid economic development at the beginning of this century, where real estate drove mass cement production, leading to increased carbon emissions. After 2011, the adjustment of industrial structure and improved production processes, such as eliminating backward production capacity and replacing high-energy-consuming kiln processes, resulted in a downward trend in carbon emissions. Due to the lack of support from long-term carbon reduction policies, the cement industry’s carbon emissions rebounded. At the beginning of 2020, the outbreak of the COVID-19 pandemic prompted strict lockdown measures across China, resulting in a significant slowdown in industrial activity during the first quarter. However, as the pandemic came under control, the Chinese government swiftly introduced a series of economic stimulus policies, notably increasing infrastructure investment, which drove a rapid recovery in cement demand. As a key industrial province, Hebei experienced a surge in cement production driven by renewed infrastructure and real estate demand. Notably, carbon emissions from the clinker calcination stage, one of the primary emission sources in cement production, cannot be substantially reduced through short-term production halts, as clinker inventories still contribute to emissions accounting. Consequently, despite the pandemic-related disruptions, carbon emissions in 2020 continued to rise under the combined influence of these factors. In 2020, China announced its strategic goals of achieving carbon peaking and neutrality, prompting the cement industry to shift toward green and low-carbon development. This transformation coincided with a sustained downturn in the real estate market, which contributed to a decline in cement demand and carbon emissions in 2021 and 2022. However, in 2023, the resurgence of infrastructure investment and a partial recovery in the real estate sector led to a rebound in cement production, resulting in a modest uptick in carbon emissions.

In terms of the specific carbon emission structure, the largest proportion of emissions comes from the decomposition of carbonates during the production process, accounting for approximately 58.4–60.1% of the total emissions. Secondly, fuel combustion emissions account for about 28.7–29.6% of the total emissions. Electricity consumption emissions make up 5–6%. Carbon emissions from the raw material extraction and transportation stages are relatively small, accounting for about 4.5% and 1.5% of the total carbon emissions, respectively.

3.2. STIRPAT Model Regression Analysis

3.2.1. SPSS Multiple Linear Regression Analysis

Based on the historical data of Hebei Province from 2005 to 2023 (

Table 3. Index data of influencing factors of carbon emission of cement industry in Hebei Province from 2005 to 2023.

Year	P	A	U	IS	T	ES	CS	B
2005	6851.00	12,845	37.69	47.00	2.26	91.82	71.55	8850.04
2006	6898.00	14,609	38.77	47.51	2.17	91.59	70.66	8492.68
2007	6943.00	17,561	40.25	48.13	1.94	92.36	70.28	9758.28
2008	6989.00	20,385	41.90	49.17	1.71	92.31	67.57	8953.00
2009	7034.00	21,831	43.74	46.81	1.66	92.51	65.30	10,684.55
2010	7193.60	25,308	43.94	47.05	1.46	89.71	63.17	12,790.21
2011	7231.86	29,647	45.59	48.05	1.31	89.09	62.02	14,533.91
2012	7262.00	31,844	46.60	47.32	1.25	88.86	59.01	13,131.84
2013	7287.59	33,346	48.02	46.08	1.22	88.69	56.69	12,747.38
2014	7322.90	34,507	49.36	45.53	1.16	88.46	56.53	10,721.46
2015	7345.20	35,994	51.67	43.64	1.17	88.83	56.58	9126.17
2016	7374.99	38,688	53.87	43.31	1.11	87.33	57.09	9898.58
2017	7409.14	41,451	55.74	41.70	1.05	86.05	60.05	9125.50
2018	7426.37	43,808	57.33	39.71	0.99	83.61	63.72	9554.30
2019	7446.56	47,036	58.77	38.29	0.93	81.96	64.98	10,527.39
2020	7463.84	48,564	60.07	37.55	0.91	80.51	65.93	11,859.97
2021	7448.00	54,172	61.14	40.49	0.81	76.58	64.53	11,354.63
2022	7420.00	56,995	61.65	40.24	0.77	73.44	65.61	10,033.94
2023	7393.00	59,332	62.77	37.40	0.75	70.20	65.00	10,130.59

), this paper conducts multiple linear regression and performs a covariance test on the relevant independent variables through SPSS 26.0 software. The results are shown in

Table 4. Multiple linear fitting results.

Model	R		R2	Adjusted R2		Standard Estimate Error
	1.000a*		0.999	0.998		0.00598
	Unstandardized Coefficients		Standardization Coefficient	t	sig.	Collinearity Statistics
	B	Standard Error	Beta			Tolerance	VIF
constant	−3.761	2.748	-	−1.462	0.178	-	-
lnP	−0.107	0.294	−0.021	−0.365	0.724	0.008	131.702
lnA	−0.060	0.051	−0.18	−1.17	0.272	0.001	926.886
lnU	0.089	0.090	0.098	0.988	0.349	0.003	385.681
lnIS	0.061	0.060	0.036	1.013	0.337	0.020	48.915
lnT	−0.042	0.068	−0.092	−0.615	0.554	0.001	873.358
lnES	0.093	0.069	0.042	1.342	0.212	0.026	38.454
lnCS	0.930	0.031	0.490	29.84	0.000	0.094	10.583
lnB	1.009	0.010	1.074	104.045	0.000	0.239	4.159

* a is represented as the predictor variable: lnP, lnA, lnU, lnIS, lnT, lnES, lnCS, lnB.

.

Table 4 shows that the R² value from the regression analysis is 0.998, and the F-test significance P is 0.000, which means there is a strong and vital relationship between the independent and dependent variables in the linear regression equation, and the model fits well. However, the significance values of lnP, lnA, lnU, lnIS, lnT, and lnES are above 0.05, and the variance inflation factors (VIFs) are much higher than 10. Therefore, there is a serious covariance relationship between the independent variables, which can lead to errors in the regression results and render them infeasible.

3.2.2. Ridge Regression Analysis

Based on the improved least squares estimation method, Ridge regression analysis is widely used to overcome multicollinearity problems [34]. In this paper, ridge regression analysis is employed to fit the coefficients of the STIRPAT model and eliminate multicollinearity issues. Ridge regression analysis is conducted on the data using SPSS 26.0 software, and the resulting ridge trace map is shown in Figure 2.

K is the ridge regression parameter. The smaller the value of k, the less information is lost in the sample, the higher the accuracy of the model, and the higher the stability. From Figure 2, when k = 0.11, the ridge trace curve gradually stabilizes, and the ridge regression coefficient is relatively large and tends to 1. In this paper, the ridge parameter k = 0.11 is selected, and the regression results are shown in

Table 5. Results of the STIRPAT model ridge regression analysis.

Variable	Unstandardized Coefficients		Standardization Coefficient	t	P	R2	Adjusted R2	F
	B	Standard Error	Beta
constant	0.242	0.282	-	0.859	0.043	0.993	0.977	F = 61.94 P = 0.003
lnP	0.178	0.028	0.178	6.14	0.008
lnA	0.205	0.033	0.211	5.97	0.009
lnU	0.135	0.056	0.033	0.626	0.035
lnIS	0.113	0.029	0.107	3.897	0.029
lnT	−0.056	0.042	−0.003	−0.081	0.094
lnES	0.126	0.031	0.121	4.039	0.027
lnCS	0.109	0.030	0.104	3.592	0.036
lnB	0.234	0.035	0.237	6.568	0.007
Dependent variable: lnI

.

Table 5 shows the model-adjusted R² is 0.977, meaning that the eight influencing factors of population size, per capita GDP, urbanization rate, industrial structure, energy intensity, energy consumption structure, cement consumption structure, and cement production can explain 97.7% of the changes in carbon emissions in the cement industry. The significant p-value is 0.003, and the p-values for most variables are below 0.05, except for lnT, which is just above 0.05. This indicates that the ridge regression coefficients of most of the independent variables pass the test at the 5% significance level and that the model performs well overall.

As shown in the ridge regression results in Table 5, the formula of the STIRPAT prediction model for the cement industry in Hebei Province is as follows:

$$\mathrm{l}\mathrm{n}\mathrm{I}=0.242+0.178\mathrm{l}\mathrm{n}\mathrm{P}+0.205\mathrm{l}\mathrm{n}\mathrm{A}+0.135\mathrm{l}\mathrm{n}\mathrm{U}+0.113\mathrm{l}\mathrm{n}\mathrm{I}\mathrm{S}-0.056\mathrm{l}\mathrm{n}\mathrm{T}+0.126\mathrm{l}\mathrm{n}\mathrm{E}\mathrm{S}+0.109\mathrm{l}\mathrm{n}\mathrm{C}\mathrm{S}+0.234\mathrm{l}\mathrm{n}\mathrm{B}$$

(9)

3.2.3. Analysis of Influencing Factors

From the regression equation, it can be observed that cement production, per capita GDP, population size, urbanization rate, energy consumption structure, industrial structure, and cement consumption structure have a promoting effect on carbon emissions of the cement industry in Hebei Province, showing a positive and increasing relationship. The coefficient of energy intensity is negative, which inhibits the carbon emissions of the cement industry in Hebei Province.

The elasticity coefficient of cement production is the highest among all factors, making it the most significant driver of carbon emissions in Hebei Province’s cement industry. Specifically, a 1% increase in cement production leads to a 0.242% rise in carbon emissions, underscoring the central role of output control in achieving energy conservation and emission reduction goals. It is suggested that the exit of backward production capacity is guided through the capacity replacement policy and, simultaneously, a capacity regulation mechanism based on carbon intensity be established.

Per capita GDP, population size, and urbanization rate collectively represent key socio-economic drivers of increasing carbon emissions. Economic growth is often accompanied by accelerated industrialization and expanded infrastructure investment, leading to heightened demand for building materials, such as cement, and, consequently, increased carbon emissions. Similarly, population growth generates higher demand for housing, transportation, and public services, further stimulating cement consumption and carbon output. Urbanization, as a critical link between economic development and population concentration, intensifies dependence on cement through infrastructure development, residential construction, and transportation expansion. This high collaboration forms a reinforcing mechanism that drives carbon emissions in the cement industry. These findings highlight the need for policymakers to integrate green building practices, promote recycling, and prioritize low-carbon infrastructure when pursuing economic and urban development goals, thereby balancing growth with emission reduction.

From the perspective of technology substitution, there is a significant synergistic effect between energy consumption structure and energy intensity. A 1% reduction in the share of coal can reduce carbon emissions by 0.126%, and a 1% improvement in energy efficiency can bring an additional 0.056% reduction in emissions. Despite some progress, coal still dominates Hebei Province’s energy structure, highlighting the need to accelerate the transition toward clean energy and gradually reduce dependence on high-carbon sources. The negative coefficient of energy intensity may reflect the combined effects of technological advancements, such as waste heat recovery and intelligent energy-saving equipment, and the adoption of cleaner energy sources. The situation underscores the crucial role of improving energy efficiency and promoting energy-saving technologies in reducing carbon emissions.

The elasticity coefficient of the industrial structure is 0.113, indicating that the proportion of the secondary industry, particularly energy-intensive manufacturing, exerts a notable influence on carbon emissions. This highlights the need to promote industrial structure optimization further and advance the development of low-carbon, high-value sectors within the tertiary industry. Additionally, the elasticity coefficient of the cement consumption structure is 0.109, suggesting that a higher clinker ratio corresponds to increased carbon emissions. As new dry-process cement technology and the concept of low-carbon building materials gain traction, reducing the clinker ratio and enhancing the use of alternative materials have become key strategies for emissions reduction within the cement industry.

To sum up, the carbon emissions of the cement industry in Hebei Province are influenced by various factors, among which production, economic growth, and urbanization emerge as the primary drivers of emission increases, while technological progress and structural adjustment serve as the main pathways for emission reduction. These findings have practical significance for Hebei Province in formulating low-carbon development strategies and provide policy references for other high-energy-consuming industries and regions across the country.

3.2.4. Regression Equation Fitting

To further verify the model’s fitting effect, the actual carbon emissions of the cement industry in Hebei Province from 2005 to 2023 are compared with the predicted values. As shown in Figure 3, the relative error of the results is within 5%, the fitting effect is good, and the obtained regression equations have practical significance. Therefore, the STIRPAT model can predict the carbon emissions of the cement industry in Hebei Province.

3.3. Carbon Emission Forecast Analysis of the Cement Industry in Hebei Province

According to the scenario parameter settings, the STIRPAT model is used to project the carbon emissions of Hebei Province from 2024 to 2035 under different scenarios, and the results are shown in Figure 4.

The results indicate that under all three scenarios, the cement industry’s carbon emissions in Hebei Province exhibit a downward trend to varying extents. The sector achieved its carbon peak in 2020, reaching 70.29 million tCO₂e. This milestone is of substantial significance, as it marks a pivotal shift from a prolonged emission growth period to a phase of steady decline.

Historical data reveal that around 2011, Hebei Province’s cement industry experienced extensive development, primarily characterized by rapid capacity expansion. Environmental awareness was relatively low during this period, and systematic carbon reduction policies had not yet been established. The disorderly expansion of the industry led to a sharp rise in carbon emission intensity per unit of output, resulting in the highest carbon emissions recorded in the province’s history. This stage underscored the severe issues of high energy consumption and heavy pollution inherent in the traditional development model. From 2011 to 2015, carbon emissions experienced a brief decline, primarily driven by short-term administrative interventions, such as production restrictions and enforced shutdowns. However, without substantial technological upgrades and sustained policy support, carbon emissions began to rebound after 2015. This trend highlights the persistence of structural contradictions within the industry, indicating that emission reductions achieved through administrative means alone are not sustainable in the long term. After 2020, under the guidance of China’s dual carbon strategic goals, the cement industry in Hebei Province entered a new stage characterized by the synergistic effect of policy direction, technological advancement, and market mechanisms. The widespread adoption of green technologies, the promotion of industrial integration, and the gradual establishment of carbon trading markets have collectively driven a sustained decline in carbon emissions. This trend is expected to continue, reinforcing both the rationality and the credibility of identifying 2020 as the carbon emissions peak for the cement industry.

The prediction results from the three scenarios further reveal that under the baseline scenario, which assumes the continuation of the current development path and emission reduction measures, carbon emissions will decrease by 25.4% by 2035, indicating that it is still challenging to achieve a rapid transformation relying solely on the current policies. In the low-carbon scenario, introducing targeted emission reduction policies and technological optimization can reduce carbon emissions to 39.77 million tCO₂e, representing a significant expansion in the reduction rate. In the enhanced low-carbon scenario, with more proactive policy guidance and technological innovation support, carbon emissions can be further reduced to 34.95 million tCO₂e, a decrease of 9.25 million tCO₂e compared to the baseline scenario, significantly enhancing the efficiency and speed of carbon emission reduction. The results of the scenario comparison clearly show that the effectiveness of carbon reduction is influenced mainly by the intensity of policy intervention and the extent of technological progress. A significant positive correlation exists between these two factors, reinforcing the critical role of policy incentives and technological innovations in achieving the carbon neutrality goal.

3.4. Sensitivity Analysis

Since there is a certain uncertainty of data in the process of carbon emission accounting, the evaluation results will fluctuate within a specific range. Thus, sensitivity analysis is needed to check how much the parameters affect the results at each stage. In this paper, the sensitivity analysis of raw material consumption is carried out by using the factor change method [35], and the specific formula is as follows:

$$\mathrm{S}={\displaystyle \frac{\mathrm{\Delta }{\mathrm{I}}_{\mathrm{i}}}{\mathrm{\Delta }{\mathrm{P}}_{\mathrm{i}}}}$$

(10)

where S is the sensitivity coefficient of factor i; $\mathrm{\Delta }{\mathrm{I}}_{\mathrm{i}}$ is the change of the corresponding environmental impact indicators under the magnitude of the change of the ith factor; $\mathrm{\Delta }{\mathrm{P}}_{\mathrm{i}}$ is the change magnitude of the ith factor. To evaluate the influence of varying raw material consumption on carbon emissions in cement manufacturing, each raw material’s variation is set at ±10% [36], with the sensitivity analysis results presented in

Table 6. Sensitivity analysis results.

Factor	Limestone	Clay	Sandstone	Iron Ore	Gypsum	Slag	Fly Ash	Coal	Electricity
Sensitivity coefficient	0.6220	0.0009	0.0021	0.0019	0.0125	0.0053	0.0047	0.2935	0.0539

.

The greater the value of the sensitivity coefficient S, the greater the influence of the influencing factor on the result in the evaluation process. According to the sensitivity analysis results, the most sensitive factor affecting the carbon emission of cement production is limestone consumption; with a change of 10% of limestone, the corresponding carbon emission will change by 6.22%. Coal and electricity consumption follow, with sensitivity coefficients of 0.2935 and 0.0539, respectively. The sensitivity coefficients for clay, sandstone, iron powder, and other factors are low, meaning they do not significantly affect carbon emissions when they change by ±10%, indicating they are not sensitive. Therefore, through sensitivity analysis, it can be concluded that the promotion of raw material substitution technology and fuel substitution technology is the primary means to reduce carbon emissions in the cement industry.

3.5. Uncertainty Analysis

Carbon emission accounting involves many activity-level data, such as energy consumption and raw material usage, and carbon emission factor data, such as fuel combustion and process emission factors. However, due to the spatiotemporal heterogeneity of data sources, measurement errors, differences in statistical methods, and other factors, these data are often uncertain, which significantly impacts the accuracy of results from carbon emission accounting. Therefore, this paper adopts the error transfer formula to conduct a quantitative analysis of the uncertainty of carbon emission accounting. The quantitative principle is taken from the “Good Practice Guidelines and Uncertainty Management for National Greenhouse Gas Inventories” and the “Provincial Greenhouse Gas Inventories Compilation Guide.” The error transfer formula for addition and subtraction is as follows:

$${\mathrm{U}}_{\mathrm{t}}={\displaystyle \frac{\sqrt{{({\mathrm{U}}_1\times {\mathrm{x}}_1)}^2+{({\mathrm{U}}_2\times {\mathrm{x}}_2)}^2+\cdots +{({\mathrm{U}}_{\mathrm{n}}\times {\mathrm{x}}_{\mathrm{n}})}^2}}{|{\mathrm{x}}_1+{\mathrm{x}}_2+\cdots +{\mathrm{x}}_{\mathrm{n}}|}}={\displaystyle \frac{\sqrt{\sum _{\mathrm{n}=1}^{\mathrm{N}}{({\mathrm{U}}_{\mathrm{n}}\times {\mathrm{x}}_{\mathrm{n}})}^2}}{\left|\sum _{\mathrm{n}=1}^{\mathrm{N}}{\mathrm{x}}_{\mathrm{n}}\right|}}$$

(11)

where U_t is the uncertainty of the sum or difference of n estimates, %; U_n is the uncertainty of the estimates with n additions and subtractions, %; and X_n is the estimate of n phase addition and subtraction.

The error transfer formula for multiplication and division is as follows:

$${\mathrm{U}}_{\mathrm{t}}=\sqrt{{\mathrm{U}}_1^2+{\mathrm{U}}_2^2+\cdots +{\mathrm{U}}_{\mathrm{n}}^2}=\sqrt{{\sum }_{\mathrm{n}=1}^{\mathrm{N}}{\mathrm{U}}_{\mathrm{n}}^2}$$

(12)

where U_t is the uncertainty of the product of n estimates, %; and U_n is the uncertainty of the estimate of n multiplied, %.

Concerning the research of Zhang et al. [37], the uncertainty of activity level data and the carbon emission factor selected in this study are 5% and 10%, respectively. The uncertainty of carbon emission results at each stage is calculated according to the formula, and the results are shown in

Table 7. Uncertainty analysis of carbon emission accounting in cement production process.

Emission Stage	Raw Material Mining	Raw Material Transportation	Process Emissions	Fuel Combustion	Power Consumption	Total Emissions
Uncertainty	8.02%	5.52%	11.18%	11.18%	11.18%	7.44%

.

The results indicate significant differences in the uncertainty of carbon emission results at each stage of cement production. These stages of uncertainty eventually accumulate and are introduced into the uncertainty of total emissions from the cement industry, which is valued at about 7.44%. The uncertainty is small and within a reasonable range, indicating that the activity level data and related parameters are reasonably selected in the accounting process of this study, which has little impact on the uncertainty of the result, and the data quality and analysis method are highly reliable.

3.6. Analysis of Emission Reduction Strategies

According to the above calculation of cement carbon emissions, carbon emissions in cement production mainly come from carbonate decomposition and fossil fuel combustion in the calcination stage of clinker, accounting for about 90% of the total carbon emissions, which is the key link of cement industry emission reduction. Based on the carbon emission accounting of cement, the analysis of influencing factors and scenario prediction, and in combination with the cement roadmap report proposed by the International Energy Agency (IEA), the following emission reduction countermeasures are proposed for the cement industry.

Energy Efficiency Improvement Technology: Energy efficiency improvements in the cement industry can be achieved by phasing out outdated technologies and promoting advanced energy-saving measures [38]. Key technologies include energy-efficient vertical mills, low-temperature waste heat power generation technology, high-efficiency clinker calcination systems, and energy-efficiency management technologies. Waste heat recovery from cement kilns plays a particularly significant role in energy conservation and emission reduction for Chinese cement enterprises. Most new dry-process cement production lines are equipped with waste heat recovery systems, and the adoption rate of this technology has exceeded 75% [39].

Raw Material Substitution Technologies: These technologies primarily focus on developing low-carbon cement by reducing carbonate-based raw materials and lowering the clinker-to-cement ratio. For example, non-carbonate materials, such as calcium carbide, steel slag, and papermaking sludge, can partially replace limestone as raw materials. In addition, developing and applying alternative cementitious materials with enhanced performance can further support emission reduction. Low-carbon cement, such as high Belite and sulphoaluminate cement, can be developed to substitute raw materials and reduce carbon emissions in cement production.

Fuel Substitution Technologies: Increase the proportion of alternative fuels, such as biomass and RDF derived from municipal solid waste, to reduce reliance on fossil energy. Research and application of fuel substitution technologies began relatively late in China, and the current thermal substitution rate remains below 2% [40], indicating significant room for improvement and future development.

Carbon Capture, Utilization, and Storage (CCUS): Given that no alternative raw materials or processes can fully replace limestone on a large scale in cement production, CCUS is expected to become an essential pathway for the cement industry to achieve carbon neutrality. It is estimated that by 2050, CCUS will need to account for approximately 50% of the sector’s total carbon emission reductions [41].

Coordinated Emission Reduction Across the Building Life Cycle: Carbon emissions can be indirectly reduced by decreasing cement demand through optimizing various stages of the building life cycle, including material production, the use of high-performance concrete, prefabricated construction methods, and structural design optimization. For instance, replacing traditional concrete with thin-walled hollow concrete blocks can significantly lower the total consumption of cement and concrete at the source [42]. This approach enhances the efficiency of cement utilization and improves the structural performance of buildings.

Policy and Market Mechanism Support: Strengthening carbon emission monitoring and evaluation, implementing carbon footprint accounting for cement products, and enhancing the carbon emissions trading system are essential. Moreover, establishing a carbon accounting and certification framework compatible with the EU CBAM is crucial. These measures will facilitate the alignment of carbon market rules between China and the EU, promoting international cooperation and improving the effectiveness of emission reduction strategies in the cement industry.

4. Discussion

According to the scenario analysis, the cement industry in Hebei Province will reduce its carbon emissions by 25.4% by 2035. However, reducing emissions may be subject to the dual constraints of technological limits and industry structure. For instance, new high-efficiency rotary kilns can significantly enhance thermal efficiency and reduce coal consumption and carbon emissions per clinker unit during the clinker calcination stage. Nevertheless, thermodynamic laws limit the theoretical minimum energy consumption for clinker calcination and cannot be infinitely reduced. From the perspective of cement consumption structure, incorporating cement admixtures (such as fly ash and slag) can effectively reduce carbon emissions. However, the maximum dosage of these admixtures is constrained by cement’s mechanical properties and durability requirements. In ordinary Portland cement, the admixture content typically does not exceed 30–40%, as excessively high proportions may compromise the performance of cement and its end-use applications. On the demand side, reductions in cement consumption are often driven not solely by technological progress but more fundamentally by shifts in the broader macroeconomic structure. For instance, saturation in the real estate market or the state-led promotion of green building systems, such as the adoption of wooden and steel structures, can significantly decline cement demand.

Therefore, although this study attempts to comprehensively incorporate various influencing factors in constructing a carbon emission prediction model based on historical data and policy objectives, certain uncertainties in practical applications remain inevitable. These uncertainties may arise from future policy adjustments, market fluctuations, and unforeseen events, which could alter the trajectory of carbon emissions. Future research should integrate the actual policy implementation process and the technological development pathways of the industry to expand further and refine the model, thereby enhancing its applicability and the reliability of its predictive outcomes.

5. Conclusions

This study calculated the carbon emissions of the cement industry in Hebei Province, constructed a STIRPAT model to identify the key influencing factors, and combined it with scenario analysis to project the carbon emission trends in the province’s cement industry from 2024 to 2035. The main conclusions are as follows:

• The cement industry’s carbon emissions in Hebei Province fluctuated from 2005 to 2023. The primary sources of emissions are process-related carbonate decomposition (accounting for 58.4–60.1%) and fuel combustion (28.7–29.6%). Therefore, efforts to conserve energy and reduce emissions should prioritize the clinker calcination stage and promote the adoption of alternative raw materials and fuel substitution technologies.
• Among the influencing factors, seven factors, such as cement production (elasticity coefficient 0.234), per capita GDP (0.205), and population size (0.178), are positively correlated with carbon emissions. At the same time, energy intensity (−0.056) suppresses the growth of emissions through technological progress. This indicates that output, economy, and urbanization are key driving forces. Technological progress and structural adjustment are the main paths for reducing emissions.
• The carbon emission prediction results indicate that the cement industry in Hebei Province peaked in 2020, reaching 70.29 million tCO₂e. Under all three scenarios, carbon emissions exhibit a downward trend to varying extents. The enhanced low-carbon scenario demonstrates the most substantial reduction, with emissions projected to decline to 34.95 million tCO₂e by 2035, a decrease of 9.25 million tCO₂e compared to the baseline scenario. This highlights the significant potential of coordinated policy intervention and technological innovation in accelerating emission reduction efforts.