Establishment of HFC-134a Emission Inventory in the North China Plain from 1995 to 2020

Abstract

1,1,1,2-tetrafluoroethane (HFC-134a) is a potent greenhouse gas that can be degraded to produce trifluoroacetic acid (TFA), a degradation product that has an impact on aquatic ecology, so its emission has been a continuous concern worldwide. Existing studies mainly estimate the global- or national-scale emissions of HFC-134a, and there are relatively few studies on regional emissions, all of which used the top-down method. By establishing a regional-scale bottom-up emission inventory and comparing it with the regional-scale top-down estimation results, regional emissions can be verified and their emission characteristics and environmental impacts can be analysed. HFC-134 emissions were estimated for the first time in the North China Plain using the emission factor method, and spatiotemporal characteristics and environmental impacts were analysed for the period of 1995 to 2020. The results showed that the cumulative HFC-134a emissions were 88 (73–103) kt (126 Mt CO₂-eq), which have led to an increase in global radiative forcing of 1.1 × 10⁻³ (0.9 × 10⁻³–1.3 × 10⁻³) W m⁻², an increase in global surface temperature of 8.9 × 10⁻⁴ °C, and a cumulative TFA production of 7.5 (6.2–8.9) kt as of 2020. The major sources of HFC-134a emissions are the refrigeration and air conditioning sector, which involves the automotive air conditioning (MAC), industrial and commercial refrigeration, and air conditioning (ICR) sub-sectors. China joined the Kigali Amendment in 2021 to phase down HFCs and proposed the goal of carbon neutrality by 2060. The North China Plain is a region undergoing rapid economic development, with a relatively high proportion of GDP (29%) and car ownership (23%) in 2020. Additionally, HFC-134a emissions accounted for about 20% of the total emissions in China. Therefore, HFC-134a emissions and their environmental impact on the North China Plain should not be ignored.

1. Introduction

As one of the main substitutes for ozone-depleting substances (ODSs), the emissions [1] and the atmospheric concentrations of the potent greenhouse gas 1,1,1,2-tetrafluoroethane (HFC-134a) (with a GWP as high as 1430) show a rapid growth trend and are regulated by two international conventions. In 1997, it was listed as a controlled substance by the International Climate Convention “Kyoto Protocol” [2] in order to reduce its emissions. In order to phase down HFC-134a, it was included in the scope of the Kigali Amendment of the Montreal Protocol (hereafter referred to as the Kigali Amendment) in 2016 [3]. In addition, HFC-134a can produce trifluoroacetic acid (TFA), a degradation product of atmospheric degradation, with a yield of 7–21% [4,5], which causes significant harm to aquatic ecosystems when aquatic concentrations are more than 100 μg L⁻¹ [6]. Therefore, HFC-134a emissions have been a continuous concern worldwide.

In 2020, China proposed the goals of carbon peaking in 2030 and carbon neutrality by 2060. In 2021, China joined the Kigali Amendment, which requires the production and consumption of HFCs to be frozen in 2024 and then phased down. The HFC emissions in China comprise a large proportion of those emitted globally, and the percentage increases year by year, attracting international attention. An accurate estimation of HFC emissions in China would be able to effectively support the country’s international compliance and its goal of carbon neutrality by 2060. Existing studies mainly estimate the total emissions of HFC-134a in China based on the national total HFC-134a consumption data by sector or limited observational data [7,8,9,10,11,12,13,14,15,16], but there are relatively few emission studies for a specific region in China. All existing regional-scale emission studies used top-down methods [17,18,19,20] based on observational data; however, no regional-scale emission estimation studies used the bottom-up emission factor method based on consumption data. Establishing a regional-scale bottom-up emission inventory will help to better reflect the temporal and spatial characteristics of emissions and their environmental impact. When compared with the regional-scale top-down estimation results, this will help to improve the accuracy of regional-scale emissions estimation and, to a certain extent, will further enhance the accuracy of national-scale emissions. The GDP and car ownership in the North China Plain account for a relatively high proportion of China’s total, reaching 29% and 23% in 2020, respectively. It is likely to be one of the most important emission regions of HFC-134a in China. This study used the emission factor method [21] to first estimate the HFC-134a emissions in the North China Plain from 1995 to 2020, and then analysed their temporal and spatial characteristics and their environmental impacts as of 2020.

2. Methods

2.1. Emission Calculation Method

This study identified sources of HFC-134a emissions, including refrigeration and air conditioning, foam, and other sectors in the North China Plain. Of these, the refrigeration and air conditioning sector involves the automotive air conditioning (MAC) and the industrial and commercial refrigeration and air conditioning (ICR) sub-sectors. Other sectors mainly involve those with smaller consumption levels than refrigeration and air conditioning and the foam sectors, as well as those with unknown consumption uses. We used the analytic hierarchy process or single-factor allocation method to obtain the consumption data for each sector and subsector based on the national consumption data of HFC-134a. Specifically, for the ICR sub-sector the analytic hierarchy was used, referring to the method presented by Han et al. (2014) [22]. For the MAC sub-sector, foam, and other sectors, the single-factor allocation method was used. The specific sources of the national consumption data, the allocation method, and the allocation factor of each subsector or sector are presented in Table S1. The HFC-134a emissions from the above-mentioned consumption processes occur throughout the life cycle of the products in question including their initial, service, and disposal processes. The service process of the refrigeration and air conditioning sector includes operation and maintenance processes. The emission calculation method of the refrigeration and air conditioning sector refers to Method 2a in the 2006 IPCC Guidelines for National Greenhouse Gas Inventories [21], and the methods of Li et al., (2019) [9], Xiang et al., (2022) [23], and Wu et al., (2022) [24]. The specific formulae are as follows:

$$E_t=E_{t,ini}+E_{t,ope}+E_{t,mai}+E_{t,dis}$$

(1)

$$E_{t,ini}=C_{t,ini}\times E{F}_{ini}$$

(2)

$$E_{t,ope}=\left(B_{t-1}+C_{t,ini}-E_{t,ini}\right)\times E{F}_{ope}$$

(3)

$$E_{t,mai}=\left(B_{t-1}+C_{t,ini}-E_{t,ini}-E_{t,ope}\right)\times r_t\times E{F}_{mai}$$

(4)

$$E_{t,dis}={\displaystyle \sum }_{x=1}^{life}\left\{\left[B_{t-1}\left(x\right)+C_{t,ini}\left(x\right)-E_{t,ini}\left(x\right)-E_{t,ope}\left(x\right)-E_{t,mai}\left(x\right)+C_{t,mai}\left(x\right)\right]\times f_t\left(x\right)\right\}\times E{F}_{dis}$$

(5)

Here, $E_t$ represents the total HFC-134a emissions in the year t. $E_{t,ini}$, $E_{t,ope}$, $E_{t,mai}$, and $E_{t,dis}$ represents the emissions during the initial, operation, maintenance, and disposal processes of the refrigerant in the year t. $E{F}_{ini}$, $E{F}_{t,ope}$, $E{F}_{t,mai}$, and $E{F}_{t,dis}$ are the emission factors for the initial, operation, maintenance, and disposal processes referring to the emission factors used in the study by Li et al., (2019) [9]. Of these, the emission factors of the MAC sub-sector are 0.5%, 6.25%, 100%, and 100%, respectively, and the emission factors of the ICR sub-sector are 1%, 10%, 100%, and 100%, respectively. The $r_t$ and $f_t$ are the maintenance rate and the disposal rate of a refrigerant in year t, respectively, referring to the study by Zeng et al., (2016) [25]. The $x$ and $life$ are the age and life of the equipment, respectively. The lifetimes of the equipment in the MAC and ICR sub-sectors are 10 and 15 years, respectively, and the annual disposal rate of the equipment was calculated based on the normally distributed life function presented by Zeng et al., (2016) [25]. $B_{t-1}$ represents the bank of refrigerant in the year t−1. $C_{t,ini}$ represents initial consumption in the year t.$C_{t,mai}$ represents maintenance consumption in the year t.

The emission calculation method of the foam sector refers to Method 1a in the IPCC 2006 Inventory Guide [21] and the study by Wang et al., (2015) [26]. The specific formulae are as follows:

$$E_t=E_{t,ini}+E_{t,ope}+E_{t,dis}$$

(6)

$$E_t=C_t\times E{F}_{ini}+B_t\times E{F}_{ope}+R_t\times E{F}_{dis}$$

(7)

Here, $E_t$ represents the total HFC-134a emissions in the year t. $E_{t,ini}$, $E_{t,ope}$, and $E_{t,dis}$, represent the emissions during the initial, operation, and disposal processes of the foam in the year t. $E{F}_{ini}$, $E{F}_{ope}$, and $E{F}_{dis}$ are the emission factors for the initial, operation, and disposal processes, which are 4%, 0.5%, and 88.5%, respectively, referring to the emission factors used in the study by Wang et al. (2015) [26]. $B_t$ represents bank of foam in the year t. $C_t$ and $R_t$ represent the consumption and disposal of the foam in the t year.

The emission calculation method of other sectors refers to Method 1a in the IPCC 2006 Inventory Guide [21] and the study by Fang et al. (2016) [13]. The specific formulae are as follows:

$$E_t=B_t\times EF$$

(8)

$E_t$ represents the total HFC-134a emissions in the year t. $B_t$ represents the HFC-134a bank in the year t. $EF$ represents a composite emission factor of 0.15%, referring to the study by Fang et al., (2016) [13].

In this study, Monte Carlo was used to analyse the uncertainty of HFC-134a emissions and their production of TFA production and their increase of radiative forcing in the North China Plain. We set the uncertainty of activity level data, emission factors, and related parameters to 10% with reference to IPCC (2006) [21]. The probability density distribution functions all follow a lognormal distribution. The uncertainty range of emissions is represented by 10% and 90% confidence intervals from 1,000,000 simulations. Note that the uncertainty introduced by the process of allocating the national consumption to the province is not taken into account. The uncertainties of TFA production are derived from the emissions and lifetime of HFC-134a, as well as the TFA production rate. The uncertainty of the radiative forcing is derived from the uncertainties of the emissions, lifetime, and the radiative forcing efficiency of HFC-134a. Additionally, the uncertainty of the TFA production rate was set to 5%, referring to the studies of Luecken et al. (2010) [4] and Kotamarthi et al., (1998) [5].

2.2. Environmental Impact Calculation Method

In this study, the environmental impact of HFC-134a emissions was analysed from two perspectives: climate impact and the production of the degradation product TFA. First, the global radiative forcing increase and global surface temperature increase caused by HFC-134a emissions are used to analyse their climate impact, and the estimation methods refer to the methods used by Fang et al., (2016) [13] and Xu et al., (2013) [27]. The specific formulae are as follows:

$$\begin{array}{cc}\hfill \Delta RF& =R{F}_{i,N}-R{F}_{i,0}\hfill \\ & =[(C_{i-1,E}-C_{i-1,0})\times exp\left(-\frac{1}{\tau }\right)\hfill \\ & +F\times E_{i,N}\times \tau \times \left(1-exp\left(-\frac{1}{\tau }\right)\right)]\times RE/1000\hfill \end{array}$$

(9)

$$\Delta T=\lambda \times \Delta RF$$

(10)

Here, $\Delta RF$ is the radiative forcing changes of HFC-134a emissions (W m⁻²) in the North China Plain. $C_{i-1,E}$ and $R{F}_{j,E}$ are the global average mixing ratio and radiative forcing when the HFC-134a emissions in the North China Plain are $E_{i,N}$ and $C_{i-1,0}$, and $R{F}_{j,0}$ are the global average mixing ratios and radiative forcing when the HFC-134a emissions in the North China Plain are 0. $\tau$ and $RE$ represent life (years) and radiation efficiency (W m⁻² ppb⁻¹) of HFC-134a [1]. $\lambda$ is climate sensitivity (°C W⁻¹ m⁻²), which is 0.8 °C W⁻¹ m⁻², referring to the method of Xu et al., (2013) [27].

The calculation method of TFA production refers to the method of Wu et al., (2014) [28], and the specific formula is shown below:

$$P_t=E/x\times R$$

(11)

$P_t$ represents the TFA production in the year t. $E$ represents the annual emission of the TFA precursor HFC-134a. $x$ represents the lifetime of HFC-134a (years). $R$ represents the TFA yield of the TFA precursor HFC-134a, which is 21% [4].

3. Results and Discussion

3.1. Temporal Trends of HFC-134a Emissions

With CFCs and HCFCs gradually being replaced by HFC-134a in the refrigeration and air conditioning, foam, and other sectors in the North China Plain, the cumulative HFC-134a emissions in the North China Plain reached 88 (73–103) kt in the period between 1995–2020. Annual HFC-134a emissions increased at an average annual rate of 30% from less than 1 kt yr⁻¹ in 1995 to 12 kt yr⁻¹ in 2020 (Figure 1a). Compared with the consumptions of different sectors, the refrigeration and air conditioning sector had the largest cumulative emissions of 53 kt, accounting for 61% of the total cumulative emissions. Followed by other sectors, which grew from less than 1 kt yr⁻¹ in 2005 to 4 kt yr⁻¹ in 2020, with cumulative emissions of 34 kt (39%). The foam sector only began to use HFC-134a as a blowing agent around 2016, at which point the cumulative emissions were the smallest (less than 1%); however, the emissions have since increased year by year. In the refrigeration and air conditioning sector, the cumulative emissions of the MAC and ICR sub-sectors were 34 kt and 19 kt, respectively, accounting for 64% and 36% of the refrigeration and air conditioning sector emissions. It is worth noting that although the emissions of the MAC sub-sector continued to increase from less than 1 kt yr⁻¹ in 1995 to 4.2 kt yr⁻¹ in 2020, the average annual growth rate slowed from 91% in 1996 to 16% in 2020. The slowdown in growth rate is mainly due to the decline in the growth rate of the production and sales of automotive air conditioners.

The emissions from various life cycle processes in the refrigeration and air conditioning sector and foam sector were further analysed here. Service emissions increased from less than 1 kt yr⁻¹ in 1995 to 6 kt yr⁻¹ in 2020 and the proportion was always the highest (72–99%) with cumulative emissions of 43 kt (Figure 1b). This was followed by disposal emissions, which increased from less than 1 kt yr⁻¹ in 1995 to 2 kt yr⁻¹ in 2020. The proportion was between 5–27%, and the cumulative emissions were 9 kt. Initial emissions were the lowest, accounting for between 1%–6%, and the cumulative emissions were less than 1 kt. Taking 2016 as an example, the emissions from the initial, service, and disposal stages were 0.05 kt, 4 kt, and 1 kt, accounting for 1%, 83%, and 16%, respectively. For the refrigeration and air conditioning sector, the service emissions included both operational and maintenance emissions, while for the foam sector, the service emissions only included operational emissions. Among the total service emissions in the refrigeration air conditioning and foam sectors, the cumulative emissions in the operating phase and the maintenance phase were 27 kt (63%) and 16 kt (37%), respectively (Figure 1b). It should be noted that the growth rate of disposal emissions has increased year by year since 2002. It can be seen that in order to reduce the HFC-134a emissions in the refrigeration and air conditioning sector, it is necessary to strengthen refrigerant recovery.

Previous studies have only estimated the top-down HFC-134a emissions in China on a national level but not on a regional level in the North China Plain. We tried to allocate the national emissions inversed by Yao et al., (2019) [29], using the Flexpart model based on observation data from each province and using GDP as the predominant factor, utilizing the allocation method of Wu et al., (2021) [24]. The calculated emissions were shown in Figure 2. Our estimated emissions are within the uncertainty range of the allocated emissions.

3.2. Spatial Distribution of HFC-134a Emissions

The North China Plain includes four provinces and two municipalities: Hebei, Henan, Shandong, Shanxi, Beijing, and Tianjin. From 1995 to 2020, the HFC-134a emissions were unevenly distributed among provinces in the North China Plain, and the cumulative emissions in Shandong Province were the highest at 29 kt, accounting for 33% of the total cumulative emissions (Figure 3a). Followed by Henan Province, the cumulative emissions were 18 kt, accounting for 17% of the total cumulative emissions. The emissions from these two provinces rose from less than 1 kt yr⁻¹ in 1995 in both provinces to 4 kt yr⁻¹ and 3 kt yr⁻¹ in 2020, respectively. Hebei Province and Beijing had similar emissions patterns, with cumulative emissions of 15 kt and 12 kt, respectively, accounting for 17% and 14% of the total cumulative emissions. The HFC-134a emissions in Shanxi Province and Tianjin were similar, with cumulative emissions of 7 kt (8.3%) and 6.6 kt (7.6%), with both comprising less than 1 kt yr⁻¹ in 2020.

The proportion of emissions from different sectors in each province and municipality was relatively stable, and the refrigeration and air conditioning sector had the highest emissions, followed by other sectors (Figure 3b). Taking the Shandong and Henan Provinces as examples, the cumulative emissions from refrigeration and air conditioning, foam, and other sectors in the Shandong Province accounted for 67%, 33%, and 0.2%, respectively, during the period of 1995–2020. The cumulative emissions of the three consumption sectors in the Henan Province accounted for 66%, 34%, and 0.2%, respectively. The total cumulative emissions from the refrigeration and air conditioning sector in the Shandong, Henan, and Hebei Provinces reached 61% of the total cumulative emissions in the North China Plain. The sum of GDP and car ownership in these three provinces were relatively high, accounting for 77% and 71% of the North China Plain, respectively; thus, the HFC-134a emissions were relatively high.

3.3. HFC-134a Environmental Impact

From the perspective of climate impact, the HFC-134a emissions in the North China Plain increased at an average annual growth rate of 29% from less than 1 Mt yr⁻¹ CO₂-eq in 1995 to 17 Mt yr⁻¹ CO₂-eq in 2020, with cumulative emissions of 126 Mt CO₂-eq (

Table 1. The HFC-134a emissions, global radiative forcing increase, surface temperature increase, and TFA production in the North China Plain.

	Emissions (Mt CO2-eq)	Global Radiative Forcing Increase (W m−2)	Surface Temperature Increase (°C)	TFA Production (kt)
1996	0.07	5.0 × 10−7	4.0 × 10−7	3.8 × 10−4
2000	0.23	6.6 × 10−6	5.3 × 10−6	8.0 × 10−3
2005	1.3	3.4 × 10−5	2.5 × 10−5	0.041
2015	10	5.2 × 10−4	4.2 × 10−4	0.59
2020	17	1.1 × 10−3	8.9 × 10−4	1.3
1995–2020	126	/	/	7.5

). By 2020, the increase in global radiative forcing brought about by the HFC-134a emissions in the North China Plain to 1.1 × 10⁻³ (0.9 × 10⁻³–1.3 × 10⁻³) W m⁻² and the contribution to global surface warming had reached 8.9 × 10⁻⁴ °C (Table 1).

The HFC-134a not only has an impact on the climate but also can be degraded to produce TFA. Our results showed that the TFA production caused by the HFC-134a emissions in the North China Plain increased at an average annual growth rate of 41% from less than 0.001 kt yr⁻¹ in 1995 to 1.3 kt yr⁻¹ in 2020, and the cumulative TFA production was 7.5 (6.2–8.9) kt (Table 1). TFA production in the refrigeration and air conditioning sector was the largest, rising from less than 0.1 kt yr⁻¹ in 1995 to 0.8 kt yr⁻¹ in 2020, with a cumulative production of 5 kt, accounting for 61% of the total TFA production. The TFA products may continue to accumulate in water bodies, causing the concentration of TFA in the water to increase year by year. When TFA water concentrations exceed 100 μg L⁻¹, it may be harmful to aquatic plants [6], and this risk should be continuously tracked and assessed. We estimated the national TFA production using the top-down emissions of HFC-134a from 2009 to 2019, as inversed by Yao et al. (2019) [29]. The results showed that the cumulative TFA production in China was 33 kt from 2009 to 2019. The cumulative TFA production in the North China Plain in this study from 2009 to 2019 was 6 kt, accounting for about 20% of the national total.

4. Conclusions

In this study, the emission factor method was used to first establish an emission inventory of HFC-134a in the North China Plain from 1995 to 2020, and the temporal and spatial characteristics and the environmental impacts caused by these emissions were analysed. The results showed that the cumulative HFC-134a emissions were 88 (73–103) kt (equivalent to 126 (104–147) Mt CO₂-eq), of which the refrigeration and air conditioning sector had the highest cumulative emissions, accounting for 61%. Comparing the different life cycle processes, the cumulative emissions in the service stage were 43 kt, accounting for 72–99% of the total emissions. It was found that disposal emissions were increasing year by year, suggesting that it is necessary to establish an effective refrigerant recovery system to reduce HFC-134a emissions during the disposal stage. Our estimated emissions are within the uncertainty range of the allocated emissions. It was demonstrated that the spatial distributions of the HFC-134a emissions in different provinces were uneven. The Shandong Province produced the most emissions, with cumulative emissions of 18 kt, accounting for 17% of the total cumulative emissions, followed by the Henan and Hebei provinces. The HFC-134a emissions in the North China Plain led to an increase in global radiative forcing of 1.1 × 10⁻³ (0.9 × 10⁻³–1.3 × 10⁻³) W M⁻², an increase in global surface temperature of 8.9 × 10⁻⁴ °C, and the cumulative TFA production of 7.5 (6.2–8.9) kt by 2020. The activity level data for this study were allocated from the available national activity level data for each sector, which may introduce some uncertainty. In the future, if the provincial activity level data for each sector can be directly obtained, the emission results will be more accurate. China joined the Kigali Amendment in 2021 and proposed the goal of carbon neutrality by 2060. The North China Plain is a region undergoing rapid economic development with high HFC-134a emissions, and so the HFC-134a emissions and their cause of environmental impacts in this region should be continuously tracked to provide scientific support for international implementation and the realization of the 2060 carbon neutrality goal.