Unveiling the Spatial-Temporal Characteristics and Driving Factors of Greenhouse Gases and Atmospheric Pollutants Emissions of Energy Consumption in Shandong Province, China

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Unveiling the Spatial-Temporal Characteristics and Driving Factors of Greenhouse Gases and Atmospheric Pollutants Emissions of Energy Consumption in Shandong Province, China


3.1. Emissions and Source Distribution of GHGs and Air Pollutants

GHGs from energy activities in Shandong Province have shown an overall rise, from 872.2 Mt CO2eq to 1017.23 Mt CO2eq in 2021 (as shown in Figure 2). This corresponds with the general upward trend in China’s CO2 emissions observed by the IEA [45]. In this study, there are some variations in GHG emissions between 2013 and 2020. The small decrease in 2013 may be related to the implementation of the air control strategy in that year, especially the emission limitation policy for highly polluting enterprises. The decline in 2020 is closely related to the impact of COVID-19 on production and life. In contrast, air pollutant emissions show a downward trend from 2010 to 2021. Emissions of SO2, NOx, PM10, PM2.5, CO, VOCs, and NH3 from 3245.47, 4846.83, 1617.29, 973.88, 7860.38, 549.94, and 37.19 kt, respectively, to 308.83, 913.00, 425.81, 273.08, 1815.00, 299.79, and 13.04 kt. Among them, the concentration of SO2 and NOx decreased most significantly, by 90% and 81%, respectively, especially in the period from 2013 to 2017, when the average annual decline reached 25% and 20%, respectively (as shown in Figure 2). The emission reduction effect of VOCs and CO has been gradually significant since 2018, which means that at different time nodes and for different pollutants, the emission reduction strategy has been timely adjusted and optimized.
Stationary combustion is the main source of GHGs and atmospheric pollutants, contributing 95% of GHGs, 90% of SO2, and over 70% of PM10, PM2.5, CO, and NH3 (as shown in Figure 3). Power plants have been using cleaner coal and increasing end-of-pipe measures to reduce emissions. The emissions of SO2, NOx, PM10, and PM2.5 have been reduced from 1335.30, 1176.19, 291.86, and 188.30 kt to 135.98, 116.70, 148.15, and 95.58 kt, achieving a reduction of approximately 89.81%, 90.07%, 49.25%, and 49.23% (as shown in Figure 3). Although power plants have implemented ultra-low emission measures, as they are the main sector of coal consumption, they still contribute more than 30% of SO2, PM10, and PM2.5 emissions. Additionally, the industrial sector, with its large-scale energy consumption, contributes to over one-third of GHG emissions and 13% and 19% of SO2 and CO emissions, respectively.
Since 2013, Shandong has been implementing the “coal-to-gas” strategy, progressively adjusting its energy consumption structure. Consequently, coal consumption in the commercial sector has significantly decreased from 8357.3 thousand tons in 2010 to 2135.2 thousand tons in 2021. However, residential coal consumption has shown a fluctuating trend, primarily due to the variability in natural gas prices and the initial capital investment required for policy implementation. As a result, some residents continue to prefer using coal, a cheaper and more reliable source of energy for heating and cooking. Between 2010 and 2021, emissions from commercial and residential sources were reduced by 90% and 50%, respectively (Figure 3). In response, the government implemented subsidy policies in 2017 to improve the central heating infrastructure and provide residents with more economical and environmentally friendly energy options. These efforts have gradually transformed energy consumption. According to the green energy development action plan of the “Hundred Townships and Thousand Villages” in Shandong Province, there is still a significant disparity in the energy consumption structure between urban and rural areas. Specifically, rural residents have a per capita coal consumption of 192 kg, which is 4.2 times higher than that of urban residents. This indicates the potential to reduce emissions.
Road sources are the main sources of NOx and VOC emissions from fossil energy consumption, accounting for 75% and 64% of total emissions, respectively. Due to the operating mechanism of diesel engines, heavy trucks have become the main source of NOx emissions, accounting for 70–85% of motor vehicle emissions. Because of their large numbers, light passenger cars are identified as the largest emitters of CO and VOCs, accounting for 51% and 62% of all vehicle emissions in 2021 (Figure 4). Despite the continuous increase in the number of motor vehicles, the total emissions of air pollutants caused by these vehicles have shown an overall downward trend, thanks to the continuous improvement of national emission standards. However, we also observed a slight rebound in pollutant emissions as the number of vehicles increased between the two updates to emission standards.

Railway turnover in Shandong Province showed a decreasing trend from 2011 to 2015. However, driven by the “road-to-rail transfer” policy, freight turnover increased from 107,728 million ton-kilometers to 168,212 million ton-kilometers between 2015 and 2021. Especially after 2018, Shandong actively implemented the Three-year Action Plan for Promoting Transportation Structural Adjustment (2018–2020), which resulted in an annual growth rate of 8.25% in freight turnover. Nonetheless, the expansion of railway electrification, along with the improvement of internal combustion locomotives and the implementation of stricter fuel standards, has resulted in a significant reduction in pollutant emissions. For example, SO2 emissions were reduced from 238.9 kt to 5.6 kt, a 97% reduction. However, there is still potential for rail transport to reduce emissions as the electrification of railroads advances and transport efficiency continues to be optimized.

In 2010, biomass combustion was the main contributor to emissions of particulate matter, CO, VOCs, and NH3, with VOCs and NH3 accounting for a third of these emissions (Figure 5). Outdoor biomass combustion was found to produce more pollutants compared to indoor combustion due to incomplete combustion, posing a considerable environmental threat at that time [46]. However, with the implementation of measures to ban straw burning, the proportion of straw burning has decreased year by year. By 2020, widespread straw burning had become rare. Simultaneously, the increased adoption of clean energy significantly reduced the reliance on direct biomass usage as fuel. According to the Notice on Further Strengthening the Improvement of Rural Living Environment jointly issued by the Shandong Provincial Development and Reform Commission and the Rural Department, the proportion of straw burning has significantly decreased. Fuelwood usage as the primary energy source in rural areas decreased to 14.8 percent in 2019, compared to 28.1 percent in 2010.

3.3. Comparison and Evaluation of Emission Inventory

In order to verify the accuracy and reliability of the inventory compilation, this study utilized two authoritative databases, namely the Carbon Emission Accounts and Datasets (CEADs) (https://www.ceads.net.cn/, accessed on 22 March 2023) and Multi-resolution Emission Inventory for China (MEIC) (http://meicmodel.org.cn/, accessed on 22 March 2023), to conduct a comprehensive comparison of GHG emission data over time. Due to the limited availability of long-term air pollutant research data, this paper chose to utilize cross-sectional data and compare it with local research findings in Shandong Province.
The GHG emission trends obtained in this study are generally consistent with the results of the MEIC database [47], but the calculated emissions are slightly higher (Figure 8). This difference is mainly due to the additional consideration of CH4 and N2O emissions in our calculations. Compared to this study and MEIC, the GHG emissions recorded in the carbon accounting database are more significant. This is primarily because the database not only includes GHG generated by the combustion of fossil fuels but also encompasses emissions from industrial production processes [48]. For example, the calcination of calcium carbonate in cement production results in a large amount of CO2.
When assessing atmospheric pollutant emissions, SO2 emissions were estimated to be 400.74 kt in this study (Table 1). This is significantly lower than the 960.29 kt reported by Zheng et al. [49], primarily because we did not include emissions from industrial processes. However, when considering only emissions from power and thermal plants, our estimates closely align with Zheng et al. [49]. The value being higher than that of Jiang et al. [50] at 214.90 kt might be attributed to increased coal consumption in our study year and the implementation of more aggressive sulfur removal measures by Jiang et al. [50].
Motor vehicles are the primary source of NOx emissions. According to Zheng et al. [49] and our data, the NOx emissions reached 763.59 kt and 618.01 kt, respectively. Both values are significantly lower than the 988.5 kt reported by Jiang et al. [50]. Jiang et al. [50] utilized a more sophisticated Copert V5 model for their estimation, which enhanced the scientific accuracy of their data. In contrast, our estimation with Zheng et al. [49] is primarily based on simplified fuel consumption, which may not fully consider important factors such as environmental background and driving characteristics.
In the study of particulate matter emissions, we observe that Zhang et al. [49] pay more attention to the contribution of industrial processes, whereas our study with Jiang et al. [50] focuses on the impact of fossil fuel combustion. Nonetheless, consistent with Zheng et al., we also consider residential activity as a key source of particulate matter emissions. These activities also emit significant amounts of CO, which primarily comes from industrial and biomass combustion. Interestingly, our emission estimates regarding biomass burning are much lower than those of Jiang et al. [50]. This may be related to the straw-burning exclusion operation conducted in Shandong Province in 2016, which effectively reduced incidents of open burning.
In terms of VOC emissions, the primary sources identified in this paper differ from those in the other two research studies. Zheng et al. [49] identify industrial sources as the major contributors. Their categorization of industrial sources includes both the combustion of industrial fossil fuels and industrial process emissions, with the latter being identified as the primary source of VOCs [30]. In contrast, Jiang et al. [50]. show a higher proportion of VOCs originating from biomass combustion. Additionally, all three studies concur that motor vehicles are a significant source of VOC emissions.

Overall, our findings are generally in agreement with previous studies that have demonstrated similar emission trends. This provides reliability validation for our research methodology and a solid foundation for future studies.

3.4. Uncertainty Analysis

The uncertainty of the emission inventory arises from a combination of uncertainties in emission factors, removal efficiencies, and activity data. In this study, the Monte Carlo simulation method was used to quantitatively evaluate the uncertainty of the estimated results, with further details available in Cai et al. [51]. This simulation was executed 10,000 times with a 95% confidence interval to derive the uncertainty ranges. The estimated range of emissions of GHGs and atmospheric pollutants is shown in Figure 5, and specific data are shown in Table S8.

The uncertainty in SO2 emissions is the smallest, primarily because the material balance method is used heavily in the evaluation process. For instance, the sulfur content in fossil fuels and the removal efficiency of devices are determined based on local data from Shandong Province. Additionally, the activity data, sourced entirely from the official statistics of Shandong Province, are highly credible. However, for pollutants such as CO, VOCs, and NH3, the uncertainty is relatively higher due to the limited availability of local data, necessitating the sourcing of some information from literature in other regions. In terms of emission sources, non-road sources and biomass combustion exhibit significant uncertainty, primarily due to the lack of extensive field surveys and the incompleteness of data regarding open straw burning sourced from the Rural Agriculture Department.

3.5. Analysis of Driving Factors

The ordinary least squares (OLS) method was used to analyze the correlation between GHGs, air pollutant equivalents, population, per capita GDP, proportion of secondary industry, energy consumption, energy intensity, and patented inventions. It was found that the variance inflation factor (VIF) of each factor was greater than 10, indicating that there was a serious collinearity problem among the model variables (Table S9). In order to prevent the distortion of the model fitted by the OLS method, the ridge regression method is used to eliminate multicollinearity between variables (Tables S10 and S11). The prediction models of GHGs and air pollutants constructed are as follows:

l n I 1 = 0.346 l n P 1 + 0.035 l n A 1 0.062 l n A 2 + 0.050 l n T 1 0.053 l n T 2 + 0.008 l n T 3 + 9.804

l n I 2 = 4.833 n P 1 0.49136 l n A 1 + 0.909 l n A 2 + 1.060 l n T 1 + 0.570 l n T 2 0.127 n T 3   + 49.591

According to the prediction model, various factors have different degrees of influence and directions on the levels of GHGs and air pollutants. However, the variables with the greatest influence are population, the proportion of secondary industry, energy consumption, and energy consumption intensity. In several studies on the influence factors of GHGs or air pollutants, the most influential factor is population, which is consistent with the research conclusion of this paper [41,52,53]. A large population leads to more production and energy consumption, but population growth pushes people to concentrate in resource-rich areas, which can promote more intensive and efficient energy use. The factors that have the least impact of patent innovation on GHGs and air pollutants may be because there is a time lag in the wide application of new technologies, which cannot be rapidly promoted, and some end-treatment technologies for air pollutants may lead to an increase in GHGs. For example, limestone reacts with SO2 to release CO2, and increased electricity demand may also indirectly increase CO2 emissions.
There is a strong agreement between the estimated value and the STIRPAT output of GHG and air pollutant equivalent emissions from energy consumption in Shandong Province. Specifically, the error rate of GHGs as a whole is maintained in the range of ±3%, while the error rate of atmospheric pollutant equivalent is located in the range of −15% to −20%, and the prediction model can be used for predictive analysis (Figure S3).

3.6. Scenario Prediction

In the five scenarios studied, the BAU and HSD failed to reach the peak of GHG emissions before 2030, while the SCP, BER, and GER successfully achieved this goal, particularly the GER, which even reached it by 2027 (Figure 9). In the BAU, Shandong’s GHG emissions are consistently increasing, with an average annual growth rate of 0.34% from 2022 to 2035. This growth rate is lower than the 1.86% observed from 2010 to 2021, but it still does not meet the country’s carbon peak target by 2030. Therefore, this scenario is clearly not desirable. For the HSD, though again not peaking before 2030, growth has slowed to an average annual rate of 0.24%.

Both the SCP and the BER achieve the peak of GHG in 2030, but their paths and peaks are different. The SCP is based on a set of medium parameters such as fertility and economic growth, while the BER focuses on the quality of life by combining high economic growth, low energy consumption, and a high volume of patented inventions. In contrast, the BER might result in 31.5 Mt CO2eq less cumulative GHG emissions in 2022–2035 compared to the peaking scenario. The GER peaks at 1009.3 Mt CO2eq of GHG emissions in 2027. However, this requires lower energy consumption, lower fertility, and lower per capita GDP growth. This suggests that sacrificing certain economic and demographic factors for environmental quality may not be a viable option in the long run.

The air pollutant equivalent showed a downward trend in different development scenarios, indicating active efforts to reduce air pollutant emissions in each scenario. However, the rate of decline varies in different scenarios. In the BAU, the rate of reduction by 2028 is lower than in the other four scenarios. Unexpectedly, after 2028, the air pollutant equivalents of the HSD and the GER will be higher than those of the BAU. This finding leads us to rethink that pursuing economic growth alone, or focusing too much on peaking carbon emissions, may not be consistent with long-term sustainability. In addition, the SCP and BER have achieved significant results in reducing air pollutants, demonstrating that striking a balance between economic growth and environmental protection is both practical and feasible. More importantly, such balancing strategies are likely to have long-term and lasting positive effects. This study highlights the need to adopt a global perspective and long-term planning in the formulation of relevant policies, integrating multiple elements to help us achieve the best balance between economic growth and environmental protection. This research result not only has reference value for future policy setting but also highlights the importance of balanced economic and environmental development and points out the direction for subsequent related research.


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