Does Drought Increase Carbon Emissions?
Evidence from Southwestern China
Jie Yang
Yijing Huang
Kenji Takeuchi

September, 2020
Discussion Paper No.2015

GRADUATE SCHOOL OF ECONOMICS
KOBE UNIVERSITYROKKO, KOBE, JAPANDoes Drought Increase Carbon Emissions?
Evidence from Southwestern China

Jie Yang, Assistant Professor
Center for Far Eastern Studies, University of Toyama
Toyama, Japan, yangjie@ctg.u-toyama.ac.jp, +81764456442

Yijing Huang, PhD Candidate
School of Physics, Peking University
Beijing, China, huangyijing@pku.edu.cn

Kenji Takeuchi, Professor
Graduate School of Economics, Kobe University
Kobe, Japan, takeuchi@econ.kobe-u.ac.jp

Does Drought Increase Carbon Emissions?
Evidence from Southwestern China
Jie Yang1 , Yijing Huang2 , Kenji Takeuchi3
1 Center for Far Eastern Studies, University of Toyama; yangjie@ctg.u-toyama.ac.jp
2 School of Physics, Peking University; huangyijing@pku.edu.cn
3 Graduate School of Economics, Kobe University; takeuchi@econ.kobe-u.ac.jp

Abstract
This study estimates the impact of the 2009/2010 drought in southwestern China on economic
activities and CO2 emissions. We focus on the economic outcomes of the power and energyintensive sectors to investigate the substitution between hydropower and thermal power during
this extreme drought. Panel data for 97,387 firms from 2006 to 2013 are used to examine theresponses of firms to this extreme climatic event. We find that severe drought reduces hydropower generation, as well as the economic outputs of energy-intensive sectors, while it
increases the power generated by coal-fired power plants. As a result, the net emissions ofcarbon dioxide between 2009 and 2013 increased by 443,425 tons. The findings suggest thatclimate disasters may increase carbon emissions, thereby posing a threat to further climatechange.
JEL classification: D22; L94; Q25; Q54
Keywords: Extreme drought; Power and energy-intensive sectors; Hydropower; Thermal
power; CO2 emissions

1

Introduction
Attaining water and energy security is a significant challenge for every country. This isparticularly true for a country such as China, which has an uneven distribution of resources
and an obvious geographical mismatch between demand and supply. For instance, the northern provinces hold only 16% of the water resources, but accounted for more than 60% of thenational amount of coal, crude oil, and electricity production in 2015 (Lin and Chen, 2017).
Furthermore, hydropower resources in the western provinces account for 81.46% of the national total, while over half of the net electricity consumption is made by the eastern coastal
provinces (Wei, 2014; Li et al., 2015). However, China’s power demand is expected to increaseby 65% by 2030 compared to 2013 (IRENA, 2016) and nine 10-GW coal-power bases will bebuilt in arid regions according to the Energy Development Strategy Action Plan 2014–2020,
which will further increase the water scarcity (Shang et al., 2016).
As a result, the government has proposed in the 13th Five-Year Plan dual control actions
that promote energy and water conservation and efforts such as energy structure adjustment
and water utilization have been directed toward the development of renewable energy. However, climate change poses immediate risks to the fresh water supply for the production ofelectricity, as droughts are becoming more frequent and severe (Eyer and Wichman, 2018).
The industrial sector is the dominant energy consumer in China, accounting for 87% of thetotal national consumption in 2013, of which the secondary industry consumes 84% (Wei,
2014). Therefore, how and to what extent climate change affects economic activities directly
or indirectly through the water–energy nexus is becoming increasingly uncertain.
This study focuses on the impact of the southwestern drought in 2009/2010, referred toas the most severe drought event over the past century in China. During this drought, precipitation decreased by 90% and the summer growing season was 1.5 °C hotter than normal(Zhang et al., 2012). The drought began in October 2009 and its impact lasted more than ayear. It encompassed the Yunnan, Guizhou, and Guanxi provinces, as well as parts of Sichuan
Province and Chongqing, affecting 60 million people and 6.5 million hectares of agriculturalland. As reported in the 2011 China Electric Power Yearbook, in 2010, the hydropower generation of Guizhou and Guangxi decreased by 14% and 25%, respectively, compared to 2009
because of the drought. Moreover, the drought reduced the operating hours of hydropower
considerably and the electricity transmitted to Guangdong province by 6 billion kWh by theend of 2010. Some provinces and cities had to implement power rationing to deal with theresulting long-term power shortages.

1

The southwestern drought thus provides an opportunity to examine the impact of persistent drought on the economic activities of the power and energy-intensive sectors. To this end,
we develop three hypotheses regarding the water–energy nexus under climate change. First,
we assume that extreme drought affects the power and energy-intensive sectors more than theother sectors because of the damage to hydropower. Second, the overall power sector may
suffer less than energy-intensive sectors because the drought will substitute hydropower withthermal power generation in the energy mix. Third, the southwest drought likely increases
carbon emissions due to this dramatic shift in the energy mix.
Recently, the water–energy nexus has received significant attention from researchers. Various studies on the tradeoffs between water and energy have been undertaken, also incorporating the future impacts of climate change. Through simulation models and life-cycle
assessments, researchers have found that water-saving technologies, the energy mix, and coaland water consumption control policies can reduce water vulnerabilities in the power industry
under climate change (Koch and Vögele, 2009; Zheng et al., 2016; Zhang et al., 2018; Shang etal., 2018). For instance, Zheng et al. (2016) examine the regional water vulnerability of thermal power using a vulnerability index, and find that the construction of mine-mouth power
plants may aggravate the existing vulnerability and turn non-vulnerable regions into vulnerable ones. Other researchers explore the relationship between the water–energy nexus andthe industrial sector. Gu et al. (2016) use input–output analysis to test the effects of energy
conservation policy on the water saving in industrial sectors. They suggest that technology
adoption in energy-intensive industries can lead to a substantial water saving effect. Further,
Su et al. (2018) assess future drought losses under a global temperature increase and predicta considerable GDP reduction in the absence of climate change mitigation. Understanding
the water–energy nexus in industrial sectors is crucial in exploring measures for adapting toclimate change.
A growing body of literature focuses on the effects of weather fluctuations on the water–energy nexus and economic activities. For example, Eyer and Wichman (2018) investigate theeffects of water scarcity on the US energy mix using plant-level data and the Palmer Drought
Severity Index; they indicate that drought is likely to decrease hydroelectric generation andincrease CO2 emissions and local pollutants in the US. McDermott and Nilsen (2014) provide empirical evidence from Germany that electricity prices are significantly affected by bothfalling river levels and higher river temperatures. Furthermore, Zhang et al. (2018) and Chen
and Yang (2019) estimate the responses of industrial output to temperature changes in China.
Specifically, Zhang et al. (2018) find a non-linear relationship between temperature and total

2

factor productivity and that high temperature has detrimental effects on output. Chen andYang (2019) conclude that adaptation to global warming may have been undertaken in hightemperature regions. In terms of empirical analysis on drought events, most studies focus on
their impacts on agriculture, ecosystems, or variations in drought patterns (Simelton et al.,2009; Zhang et al., 2012; Alauddin et al., 2014; Wang et al., 2015; Parida et al., 2018).
As such, few researchers note the association between persistent drought and the outputsof the power and energy-intensive sectors. Moreover, it is not clear how firms respond to
the long-term water and power shortages due to drought and the subsequent environmental
consequences. Our study fills this literature gap by estimating the relationship between thewater–energy nexus and economic activities in the context of persistent drought. We use panel
data for 97,387 firms from 2006 to 2013 to examine the impact of extreme drought on energyintensive industries and power production in regions with a high hydropower share. Second,
we investigate the heterogeneous effects of long-term water shortages on firms’ responses acrossindustries, regions, and years using difference-in-difference-in-differences (DDD) models. Finally, we estimate the environmental impact of the southwest drought by calculating the net
carbon emissions of industrial sectors.
The remainder of this paper is organized as follows. Section 2 overviews China’s westerndevelopment strategy and the southwestern drought. Section 3 describes the treatment design and estimation strategy. Section 4 explains the data and sampling procedure. Section 5
presents the empirical results. Section 6 discusses the environmental consequences of drought
on industrial sectors. Conclusions based on our findings are presented in the final section.

2

Background
Severe drought is one of the most devastating natural disasters in China. Drought-affectedareas have significantly increased and extreme droughts have occurred frequently since 2000
(Xu et al., 2015). Extreme droughts affecting more than 10 million people only occurred fivetimes between 1983 and 1999; however, the number of occurrence doubled between 2000 and2016. Particularly, the extreme droughts in 2010 and 2011 caused severe water shortages in
the affected regions. Furthermore, the growing water scarcity and competition for water supply have transferred the impact of droughts to distant cities. Although an extreme drought
does not affect the industrial sector directly or immediately, it causes long-term water shortages, high temperatures, and eventually lowers productivity. Over the past 10 years, annual
industrial losses from drought have exceeded 230 billion yuan (Zhang et al., 2012). As climate3

change is predicted to further decrease precipitation and increase drought risk, more frequent
drought events and of longer duration are projected in the southwestern river basins (Xu etal., 2015; Huang et al., 2018).
Although the southwestern provinces of China are water-rich areas, they are also vulnerable
to extreme droughts. The southwestern region1 holds most of the hydropower resources in
China, comprising approximately 67% of the total national exploitable hydropower resources(Liu et al., 2018). China has promoted hydropower development in the southwestern region
and has implemented the Western Development and the West–East Electricity Transmission
Project in 2000. This project is designed to optimize the distribution of China’s resourcesand electric power structure to ease electricity shortages in developed regions by exploiting
the resources in the southwestern region (Ming et al., 2013). The southern route of the West–
East Power Transmission project transmits Guizhou’s thermal power and the hydropowerof Yunnan, Guizhou, and Guangxi to the Pearl River Delta (Guangdong, Hong Kong, andMacau). In 2010, both the transmission capacity and electricity amount of the southernroute were 10 times higher than those of 2000, with the maximum power capacity of westernelectricity to Guangdong accounting for 27% of its maximum load and 30% for Guangxi (Minget al., 2013). Moreover, as a cost-effective clean energy, hydropower plays a prominent role in
China’s 12th and 13th Five-Year Plans to achieve the commitment to reduce greenhouse gas(GHG) emissions.
[Table 1]
The development of hydropower may make the southwestern regions more vulnerable todrought for several reasons as follows. First, various supporting policies and cheap electric hydropower are attractive to firms in energy- and water-intensive industries. Pollution-intensive
industries are concentrated in the southwestern region, which may cause water scarcity due
to water quality deterioration. Second, industrial agglomerations reliant on hydropower are
more easily affected by drought-related water scarcity. According to Table 1, the share of thepower and energy-intensive firms in the southwestern region is 8% higher than that in otherregions. Third, climate change will increase drought risk, meaning more thermal power plants
are needed as alternatives for power generation, which will further stress the water–energy
nexus in this region. Finally, local policies balancing both power interests and environmental
concerns are not sufficient, which can have a significant influence on water exploitation decisions (Kosnik, 2010). In the southwestern region, the annual investment in water saving isless than half of that in other regions, and the water resource fee is also significantly lower(MOHURD, 2006–2013).
4

3

Estimation Strategy

3.1

Treatment Indicators

To examine the impact of drought on industrial activities and the energy mix, we propose
three treatment indicators for the DDD analysis. We use the first treatment variable, Drought,
to capture the effects of being a province hit by the southwestern drought. The second
treatment variable, Post, indicates the period after the drought, and the third treatment
variable, Energy, captures the effect of being a firm in a power or energy-intensive sector.
We interact these three treatment variables to identify the effect of being a power firm or anenergy-intensive one in a drought-affected area.
[Figure 1]
Figure 1 shows the geographical division of the treatment and control groups. The treatment group includes five southwestern provinces: Sichuan, Chongqing, Guizhou, Yunnan, andGuangxi. Regarding the control group, our primary concern are the impacts of other severe
droughts between 2006 and 2013. As mentioned in Section 2, two extreme droughts occurred
during our study period, that is, the southwestern drought and the 2011 drought in northern China, both of which caused significant damage and water shortages. However, the 2011
drought affected most wheat-producing regions and, thus, its influence on the industrial sector was limited. To control for the impact of the 2011 drought, we divide the control group
into two groups to distinguish the affected provinces. We use both groups as controls in thebaseline regression, and then use only group 1 as a control for the robustness check. We keep
Tibet in control group 2 because only 1% of firms are located in Tibet. According to the
EM-DAT database, the southwestern drought started in October 2009 and, therefore, we set
2008 as the baseline year.

3.2

Industrial Outcome Model

We estimate the effect of the southwestern drought on the industrial outcomes of the powerand energy-intensive sectors using the following equation:
LogYijt =X

β e (Droughtj × P ostt × Energyie ) + δjt + θnt + λnj + µi + ηt + εijt

e∈E∈n

(1)

where LogYijt is the logarithm of the industrial outcome of firm i in province j in year t,
and includes two outcome measures: the firm’s output and revenue. Droughtj is a province
5dummy that equals 1 if province j is located in the southwest. P ostt is a dummy variable
that indicates the post-drought period; it equals 1 if t>2008, and 0 otherwise. Energyie is adummy indicator for each firm’s industry type e, which is included in the two-digit industry E(power and energy-intensive sectors) (see Table A1 in the Appendix for detailed informationon the industry classification codes). n represents all two-digit industries, n=06, · · · , 46.
We include δjt , θnt , and λnj to capture the effects of unobservable regional policy, industrial
regulation, and economic shock in a given province and year, industry and year, and industry
and province. Firm fixed effect µi controls for unobservable time-invariant firm characteristics.
ηt is a year effect common to all firms in period t, which control for national energy policy
and environmental regulation over time. εijt denotes the error item. We allow for correlation
within two-digit industries and spatial correlation across industries within a given province
and year by clustering the standard errors.
Our interest is in the parameter of the triple difference term, β e . We examine the separate
effects of drought on energy-intensive and the power sectors. In our analysis, energy-intensive
sectors are defined as five two-digit energy-intensive manufacturing industries (see Table A1
in the Appendix for details). The power sector in one region could be less affected thanthe energy-intensive sectors during a drought, depending on the energy mix of that region.
If the alternative electricity resource is sufficient, the power sector can maintain electricity
production to meet urgent needs. The power sector is comprised of five four-digit electric powerindustries, including the hydropower and thermal power industries. We combine the nuclear
power and wind power industries with other electric power industries because of the limited sizeof the latter. We further investigate the heterogeneous effects of drought on the hydropower,
thermal power, and other power industries. We assume β power > β energy−intensive ; β hydro <0; β thermal > 0; β other > 0.
Furthermore, to examine how treatment effects vary across drought-affected provinces andyears, we use equation (2) to estimate DDD models with multiple provinces or time periods:
LogYijt =5XX

β e (Droughtj × P ostt × Energyie ) + δjt + θnt + λnj + µi + ηt + εijt (2)j =1 e∈E∈nwhere j=1, · · · , 5 for Guangxi, Chongqing, Sichuan, Guizhou, and Yunnan, respectively. For
the estimation considering multiple time periods, we replacej =1 with

P5

t=−2 , where t=-2,

P5-1, 1, 2, 3, 4, 5, indicating the 2 years befpre the baseline—2006, 2007—and the 5 years after
the baseline—2009 to 2013. For these two analyses, apart from output and revenue, two more
dependent variables are added, namely, employment and assets. Employment indicates the6average annual number of employees, thus indicating the scale and growth potential of a firm.
Assets indicate the firm’s total assets, including fixed and current assets.

4

Data
We use several data sources for the empirical estimations. The main data source is the Na-tional Bureau of Statistics’ (NBS) Annual Industrial Firm Survey. We construct a balanced
firm-level panel dataset from 2006 to 2013 to estimate the industrial outcome model. The
dataset includes 97,387 firms and 779,096 observations from 41 two-digit industries. Droughtdata were obtained from the EM-DAT database, which contains data on the occurrence, location, and effects of severe droughts in China. We utilize this database to identify otherprovinces affected by severe droughts other than the southwestern drought during our study
period.
The NBS industrial firm survey covers all industrial enterprises above a designated size
from 1998 to 2013. The definition of designated size changed twice, in 2007 and 2011. From1996 to 2007, the survey covered all state-owned enterprises (SOEs) and other enterprises witha main annual business revenue of 5 million yuan or more. From 2007 to 2010, SOEs under
this designated size were excluded. Then, the threshold for an industrial enterprise increased
to 20 million yuan from January 2011. Because we use panel data from 2006 to 2013, thefirms in our sample are industrial enterprises with a main annual business revenue of 20 million
yuan or more.
Several variables can be used to identify a firm: unique matching identification (ID), firm
ID, administrative code, industry code, firm name and legal representative. Each firm has
a firm ID, but firms can change their IDs as a result of restructuring, acquisition, or mergers (Zhang et al., 2018). We use the unique matching ID to merge firm data over time.
Furthermore, to ensure that the matching ID does not change similarly to the firm ID, wefollow previous studies to match firms based on administrative codes and firm names, as wellfor double-checking (Zhang et al., 2018; Chen and Yang, 2019). Our dataset includes threesecondary industry sectors, namely mining and quarrying, manufacturing, and utilities, and41 two-digit industries, which is consistent with the China Industry Classification System
GB/T4754-2011. Each firm is attributed a four-digit China Standard Industrial Classification(CSIC) code, which is used to identify its industry sector.
Most firms in energy-intensive sectors are located in the eastern coastal provinces in thecontrol group and in Sichuan Province in the treatment group. Further, the agglomeration7

of firms in energy-intensive sectors is highly power-driven. Although nuclear power and renewable energy have developed rapidly over the last decade, thermal power and hydropower
still dominate the energy mix. Large thermal power firms are concentrated in the eastern
coastal region and central China, while around half of the large hydropower firms are in thesouthwestern region.
An inherent weakness of the dataset is the large amount of missing data in the surveys
after 2007. The survey in 2010 contains only data on revenue, employment, and total assets.
Moreover, we were able to collect data on the annual average number of employees before 2010
but, after 2010, only the total number of employees at the end of the survey year is available.
Therefore, we collect seven years of data on output and eight years of data on revenue, employment, and total assets as dependent variables. We drop unreasonable observation, suchas negative values for our dependent variables and firm age. The rest of the data are used tocompile an eight-year balanced panel dataset for the analysis.
According to the summary statistics in Table 1, the average share of power and energyintensive firms is 18.8% in the control group, among which 16.8% are energy-intensive firms
and 2% are power firms. In the treatment group, the average share of firms in energy-intensive
sectors is 22% and that in the power sector is 7%. The southwestern region has less power andenergy-intensive firms in absolute value, but their shares are larger. Moreover, the share ofhydropower firms in the treatment group is more than 10 times larger than that in the control
group. According to the results, 40% of power firms are hydropower firms in the treatmentgroup, compared to 10% in the control group.

5

Results

5.1

Baseline Results[Table 2]

We report the baseline regression results in Table 2 by estimating equation (1) for thefull sample and including control groups 1 and 2. The estimated effects of the southwestern
drought on the log output and log revenue are reported. We control for province and year fixed
effects, province and two-digit industry fixed effects, year and two-digit industry fixed effects,
and year fixed effects in all the models. Firm fixed effects are included in models (2)–(4) and(6)–(8).
The results in columns (1)–(4) show that the southwestern drought had a negative but
8insignificant impact on the output of the power and energy-intensive sectors, while the effecton energy-intensive firms is negative and significant at the 5% level. Conversely, the drought
had a positive but insignificant effect on the power sector. Moreover, the southwestern drought
had heterogeneous effects on four-digit electric power industries: it reduced the output ofhydropower firms by 20.1%, but increased the output of thermal power firms and other powerfirms by 34.6% and 52.3%, respectively. Columns (5)–(8) present the estimated effects on
the logged revenue of firms in the power and energy-intensive sectors. Overall, the powerand energy-intensive sectors bear a significant loss, of 5.9%. The revenue of energy-intensive
sectors decreased by 7.6%, while the effect on the revenue of the power sector is negative
but insignificant. The revenue loss for hydropower firms was 21.6%, larger than the change
in the output, whereas the other power firms experienced an increase of 21.3% in revenue.
Interestingly, the southwestern drought is associated with a reduction of 8.8% in revenueof thermal power firms, despite the increase in output. Several reasons could explain thisdifference. First, the positive impact on revenue did not last, which we will investigate laterwith multiple-period regressions. Second, electricity may have been primarily provided fordisaster relief during the drought, which may have led to a decrease in the sales of thermal
power firms. Finally, the energy market status, such as seasonal changes in coal prices andthe lack of coal reserves, could also influence revenue.

5.2

Robustness Check

Although the southwestern drought can be regarded as a random shock, we are concerned
about the potential self-selection bias in our sample. As mentioned above, the three mostdeveloped economic zones2 in central and eastern coastal China have always been the first
options for firm locations, especially for large private firms. Meanwhile, the West Triangle Economic Zone in the southwest is still being designed as a component of the Western
Development policy. We have reasons to believe that the firms that move to or start fromsouthwestern China might have different firm-level characteristics compared to the firms inother regions. For instance, southwestern China may have more SOEs to provide additional
job opportunities.
We use the nearest-neighbor matching method to address this issue. Three firm characteristics are selected as covariate variables for matching: firm age, industry type, and ownershiptype. We keep the firms from the baseline year (2008) and employ one-to-one nearest neighbormatching with replacement using a 0.05 caliper to reduce the likelihood of poor matches. Additionally, balancing tests after matching are performed to check whether firms in the treatment
9and control groups are well balanced. There is no statistically significant difference between
the treated and control means after matching, and the bias of all covariates is reduced to
below 10% (see Table A2 in the Appendix for the balancing test results). Only 19 firms in
the control group have been dropped and the estimation results after matching are similar tothe baseline results.
[Table 3]
We run another estimation to control for the possible impact of the extreme drought in
2011, and the estimated effects are reported in Table 3. The results remain robust when wedrop control group 2, which implies that the influence of the 2011 drought is too small to beconsidered, and our results are robust across the different samples. Consequently, the southwestern drought is associated with an average decrease of 7.8% in output and 7.6% in revenue
for the energy-intensive firms in the drought-affected regions, that is, an average of 11.7 million yuan and 12.7 million yuan3 per firm, respectively. In the power sector, hydropowerfirms sustained the most severe damage, and output and revenue were reduced, on average,by 20.1% and 21.6%, or 16.8 million yuan and 18.1 million yuan, respectively.4 By contrast,
the drought increased the output of thermal power firms by 34.6%, but decreased revenue
by 8.8%, that is, a 224.5 million yuan increase in output and 57.4 million yuan decrease inrevenue 5 . As for other power firms, drought is associated with a significant increase in both
output and revenue, although the absolute size of the increase is not as large because of their
low share in the total output.
In conclusion, the effect of the drought on the power and energy-intensive sectors is negative, while the effect on the power sector is insignificant because of the substitution between
the hydropower and other power sectors. Our findings are consistent with those of Eyer andWichman (2018) in that water scarcity will likely shift the U.S. energy mix from relatively
water-intensive generation towards alternative sources. Further, a devastating drought likethe southwestern drought induces more thermal power production.

5.3

Heterogeneity Across Firm Ownership TypeTo investigate the heterogeneous impacts of drought on firms with different ownershiptypes, we collect information on firms’ ownership types to perform more detailed estimations.
There are six firm ownership types for firms: SOEs; COEs; private firms; foreign firms; Hong
Kong, Macao, and Taiwan firms; and mixed-ownership firms (Chen and Yang, 2019). Hong
Kong, Macao, and Taiwan firms are considered foreign firms according to the NBS definition.
10

Our analysis does not include mixed-ownership firms owing to their lack of clarity.
Several studies have shown that different firm types (by ownership type) tend to behave
differently to external shocks. For instance, Zhang et al. (2010) investigate how ownershipaffects corporate philanthropic responses to the 2008 Sichuan Earthquake, and find that nonSOEs are more strategically motivated and economically driven to engage in philanthropic
responses than SOEs. Cai et al. (2016) study heterogeneity among different firms in response
to environmental regulations. They find that private firms are cost sensitive and less environmentally conscious than SOEs and foreign firms, and take more action to avoid stringent
policies. Zhang et al. (2018) examine the heterogeneity in temperature effects across firmownership types, and suggest that the effects of high temperatures on output may be stronger
for private firms than for SOEs because of the weaker enforcement of labor regulations in theformer. Furthermore, the magnitude of the temperature effect is linked to the sample shareof each ownership type.
We examine the effects of the southwestern drought on the outputs and revenues of SOEs,
COEs, privates firms, and foreign firms to infer their possible responses to the drought event.
The share of each type in the balanced firm sample is 6.88%, 4.55%, 66.07%, and 19.52%, respectively. In the control group, the respective shares are 6.22%, 4.49%, 65.80%, and 20.57%.
In the treatment group, the shares are 15.58%, 5.36%, 69.65%, and 5.53%, respectively. However, SOEs comprise the largest share in the power sector of the treatment group: 56.41% forhydropower and 85.83% for thermal power.
[Table 4]
The results are presented in Table 4. Columns (1)–(8) show the estimated effects of thesouthwestern drought on output and revenue. The drought reduced the output and revenueof the SOEs in energy-intensive sectors by 12.1% and 10.4%, respectively, but only the effecton output is statistically significant at the 10% level. Private firms experienced less damage
in terms of output and revenue, although they comprise the largest share in the sample.
Conversely, foreign firms bore the largest loss according to columns (7) and (8), despite theirshare being the smallest. As implied in prior studies, private firms are more cost-sensitiveand profit-driven and, thus, they may respond negatively to power rationing during drought.
Another possible reason is that many private firms are located in urban areas that suffered lessdamage from the drought. Foreign firms sustained the worst damage, although their sample
share was the smallest in the treatment group. This is because they are more likely to follow
the power rationing policy and do not have enough capacity and resources to deal with extreme
drought, which may lead to significant production damage. In the hydropower industry, both11

SOEs and COEs (columns (1) and (2)) incurred large losses, of more than 20%, in output andrevenue. Meanwhile, the results for thermal power firms in columns (5) and (6) show thatthe drought decreased the revenue of SOEs by 12.2%, but increased the revenue of COEs by
24.5%. Compared to private and foreign firms, SOEs are less cost-sensitive and more locally
based (Cai, 2016). As such, they may play the role of a safety net during drought to adjust
the energy market rather than make profit. COEs are also locally based, but are more flexible
and have a less significant social purpose. Similar to the energy-intensive sectors, private firms
in the hydropower industry experienced the least loss, but benefitted most from the thermal
power industry, whereas foreign firms incurred the largest loss in almost all industries.

5.4

Effects on Power Production by Province and YearThe effects of drought vary across provinces, and firms in the worst affected provinces may
suffer more damage. Furthermore, industry structure, the energy mix, power plant scale, andinfrastructure in a province may affect firms’ responses to long-term droughts. We present
the results of estimating equation (2) in Tables A3 and A4 in the Appendix. Figure 2 shows
the heterogeneity effects of the southwestern drought on output, revenue, employment, andassets. We find that the energy-intensive sectors in Sichuan and Guizhou were more affected bydrought because relatively more energy-intensive firms are located here. The hydropower firms
in all provinces were significantly affected. The southwestern drought shifted the energy mix
in this region from hydropower towards thermal power generation, especially in Guizhou andYunnan. The hydropower’s share in Guizhou’s energy mix was much smaller than in Yunnan
and, thus, the southwestern drought induced a large increase in its thermal power production,
namely 187.2% for output and 82.1% for employment (Table A3, columns (9) and (11)).
Although Chongqing sustained less damage from the drought, there was a significant increaseof 51.3% (Table A3 column (11)) in the employment of its thermal power firms there becauseof its lower hydropower share and large electricity demand. This shift was not observed in theother two provinces, but the outputs and revenues of their hydropower firms were negatively
and significantly affected. Sichuan Province is very close to the three largest hydroelectricity
power stations in China—the Three Gorges Dam, Xiluodu Dam, and Xiangjiaba Dam—whichmakes it more adaptable to extreme droughts. The southwestern drought also decreased theassets of firms in all industries. The consumption of inventory for production and decrease inlong-term investment during the drought can explain this.
[Figure 2]
12[Figure 3]
The estimated results over time are summarized in Table A4 in the Appendix. The drought
is associated with a reduction in the output, revenue, employment, and assets of energyintensive and hydropower firms since 2009. Figure 3 reveals the estimated effects on power
production over time. Specifically, Figures 3-(a) and 3-(b) show the effects on the output
and revenue of the power sector. We find a similar trend in these two figures. The negative
effects on output and revenue of hydropower firms lasted 5 years from 2009 to 2013, andthe largest decrease of 21.6% (Table A4 column (5)) in output was recorded in 2012 while
a decrease of 28.1% (Table A4, column (6)) in revenue was observed in 2011. However,the positive effects on the output and revenue of thermal power firms lasted 3 years, with thestrongest effects being registered in 2009, immediately after the drought, after which the effects
became weaker. Figure 3-(c) shows that the southwestern drought decreased employment forhydropower firms, but their employment was quickly restored immediately after 2011. The
effects on the employment of thermal power firms lasted longer and peaked in 2011. Finally,
the negative effects on assets lasted for a longer time and showed no sign of dissipating. Longterm investment in the power sector in the southwestern region could have been influenced
by drought. We also explore the effects of drought on the production of other power firms,
but the results are not shown in the figures because the sample share is too small to reveal
long-term effects. The other power firms, including nuclear power, solar power, wind power,
and other renewable energy firms, responded positively and quickly to the drought, whichmeans they could be good alternative sources of power generation in the short term.

6

Environmental Impact

6.1

Carbon Emissions by the Southwestern Drought

The southwestern drought affected not only the economic activities of the power andenergy-intensive sectors, but also the GHG emissions in the drought-affected regions. We
hypothesize that the drought led to more GHG emissions in the southwest due to the higher
thermal power generation. However, the overall effect on emissions is not clear. Although
the number of thermal power firms is much smaller than that of energy-intensive firms, wehave two reasons to believe that the thermal power sector plays a more decisive role in net
emissions. First, the average output (2006–2013 with full sample) of thermal power firms inthe southwest is much higher than that of energy-intensive firms—938 million yuan versus
13276 million yuan (outputs are deflated to 2015 values using the GDP deflator). Second, theemission intensity of the power sector is much higher than that of the other sectors.
Previous studies have estimated China’s regional and sectoral CO2 emissions using various
approaches. Due to the uncertainty of China’s energy statistics, most researchers (Peters etal., 2006; Liu et al., 2012; Liu et al., 2015; Shan et al., 2016) propose new energy consumption
data for CO2 estimations. For instance, Liu et al. (2015) adopt the apparent consumption
method and update emission factors to recalculate China’s CO2 emissions. They find that theemission factors for Chinese coal are, on average, 40% lower than the IPCC default values andChina’s cumulative CO2 emissions from fossil fuel combustion and cement production havebeen overestimated for 2000–2013. Shan et al. (2016) follow the previous study and recalculate
the Chinese provincial CO2 emissions and discuss emissions from different sectors. They divide
the fossil fuel CO2 emissions for 16 sectors based on the energy balance table. They find thatmost CO2 emissions are produced by thermal power, industrial final consumption, petroleum
refineries, and coal washing.
Building on previous studies, we extract energy consumption data from the energy balancetables in the China Energy Statistical Yearbook (CESY) for the five southwestern provincesfrom 2006 to 2008. Our calculation of sectoral emission intensity is based on the mass balanceof energy and the accounting process proposed by Liu et al. (2015) and Shan et al. (2016). The
estimation of CO2 emissions is conducted in two steps. The first step is to calculate the sectoral
emission intensities6 in the five southwestern provinces from 2006 to 2008. Unlike the energybalance table, the sectoral output in the statistical year books contains 39 industrial sectors.
Therefore, the energy used for the power sector (two-digit industry code=44) is measured by
the energy use of thermal power and heating supply sectors based on the “input & output oftransformation” sectors in the energy balance table. The energy used for processing petroleum
and coking (industry code=25) is measured by the energy use of petroleum refineries andcoking sector based on the “input & output of transformation” sectors in the energy balancetable. Furthermore, the energy used for the other four energy-intensive sectors (two-digit
industry code=26, 30, 31, 32) is measured by the energy use of the industry sector (nonenergy use is excluded) based on the “final consumption” sectors in the energy balance table.
The second step is to combine the sectoral emission intensities with our DDD estimation
results to estimate the change in sectoral CO2 emissions caused by the drought.
3
44 X
Emissionj =×
ADij × EFi
12 n=114(3)

Equation (3) is the general form used to calculate the sectoral CO2 emissions in the firststep. ADij is the activity data, that is, the energy consumption of primary fuel type i in
physical units (metric tons) in sector j, i=raw coal, crude oil, and natural gas; j=thermal
power and heating supply, petroleum refineries and coking, and industry sectors. EFi is the
updated carbon emission factor for primary fuel type i, which is assumed to be the same acrossprovinces and time.
[Table 5]
Table 5 reports the sectoral emission intensity of each southwestern province during 2006–
2008. The emission intensities show a tendency of decreasing and the thermal power andheating supply sectors have much higher average emission intensities than the other sectors. Moreover, poorer provinces (measured by total output) have higher emission intensities,
which is consistent with the results of previous studies. Energy intensities also vary acrossprovinces. Liu et al. (2012) use final the input/output consumption method to obtain the
sectoral/regional CO2 emission intensity during 1997–2009. Compared to their results, someof our emission intensity values in 2006 and 2007 seem unreasonable. Therefore, the sectoral
emission intensities in 2008 are used to calculate the CO2 emissions caused by the drought.
Additionally, we use the average value of emission intensities for petroleum refineries and coking and industry sectors as a proxy for energy-intensive sectors’ emission intensity.
Using the estimated coefficients in column (4) of Table 2, we calculate that the southwestern drought caused a decrease of 7.8% in the output of the energy-intensive firms, or 17,339.4
million yuan,7 and an increase of 34.6% in the output of thermal power firms, or 6,959.5 million yuan.8 Since the emission intensities of these two sectors are 3.77 and 10.03, respectively,
the CO2 emissions from the energy-intensive sectors decreased by 6,536,954 tons, whereas theCO2 emissions from the power sector increased by 6,980,379 tons. Therefore, the net CO2
emissions from fossil fuels in 2009–2013 in the southwest increased by about 443,425 tons as
a result of the extreme drought.

6.2

Discussion

This study does not consider the improvements in technology and energy efficiency after
2008, which may lead to an overestimation of the carbon emissions due to the southwestern
drought. The net carbon emissions depend on the amount of the increase in the thermal power
sector and decrease in the energy-intensive sectors during the drought, for which provincial

15

sectoral energy intensity is the determinant factor. Over the past two decades, high-energyconsuming and low-end manufacturing industries have gradually transferred from the easternregion to the central and western regions due to a series of policies issued by the Chinese
government to promote industrial transfers and regional structure adjustments (Zhao andLu, 2019). Because the western region is rich in energy resources and the energy price islower than in other regions, enterprises usually choose to input more energy in the production
process (Tan and Lin, 2018). This results in the higher energy intensity of the firms in thewestern region compared to other regions (Dong et al., 2018; Tan and Lin, 2018; Zhao andLu, 2019), which consequently increases the energy vulnerability of the former region. Policies
promoting energy conversion and improving the technical efficiency of the power and energyintensive sectors are thus crucial in mitigating the negative environmental impacts of extreme
drought.

7

Conclusions
This study has examined the impact of the southwestern drought on the industrial out-

comes of the power and energy-intensive sectors. By using an 8-year firm-level balanced panel
data, we explored the responses of power and energy-intensive firms to the long-term waterand power shortages caused by the extreme drought. We found that the impacts of the southwestern drought are heterogeneous across sectors. In the southwestern region, firms in theenergy-intensive industries sustained more damage during the drought than those in the power
sector. Meanwhile, the accumulative effects on the power sector depend on the regional energy
mix. Since the long-term water shortage affected hydropower generation, the southwestern
drought generated more thermal power. Our findings suggest that energy-driven industries
are vulnerable to extreme climate changes, especially in regions with a high dependence onwater resources. It might thus be necessary to include drought adaptive measures in the development policies for water-abundant areas.
We also find that ownership type affected firms’ responses to the disaster event. In particular, collectively-owned and private firms in the power and energy-intensive sectors wereaffected least by the southwestern drought. They adopted two behavioral patterns in response
to policies such as powering rationing during the drought. If they responded positively, they
may have developed adaptive behaviors towards disasters such as droughts. However, if they
responded negatively, they may have simply ignored the policy and caused more damage tothe total welfare. Moreover, foreign firms sustained the largest damage during the drought,

16which could influence foreign investment. Conversely, SOEs may have played a more social
or political role during the drought. The distribution of firms’ ownership types in one region
could also be an important consideration for disaster management policies.
We further estimated the environmental impact of the southwestern drought. Our results
imply that the drought not only had a detrimental impact on economic activities, but also
led to CO2 emissions of around 443,425 tons more from the thermal power sector. As theworld’s largest carbon emitter, China plays an important role in global climate change mitigation and has made commitments to achieve specific mitigation targets by 2030 (Shan etal., 2016; Zheng et al., 2019). However, extreme weather and natural disaster risk undermine
these efforts against climate change. Our regression analysis provides evidence that natural
disasters such as extreme droughts will likely cause economic loss, while increasing carbon
emissions. Policies aiming to reduce the energy vulnerability to climate change during clean
energy transition must be based on a solid understanding of what constrains regional industrial sectors in terms of clean energy adoption.
Our study has several limitations. First, the external validity of the findings is limited as
we focus on extreme droughts in regions with high shares of hydropower. Future studies could
thus explore the impacts of different disaster types on power industries and the energy mix.
It is possible that extreme droughts have different effects on the power and energy-intensive
sectors in arid regions. Second, missing data may have resulted in estimation bias. The main
dependent variables of output and assets have a large amount of missing data, while the employment variable is not consistent before and after 2010. More thorough surveys are therefore
needed for detailed analyses. Finally, this study focuses only on production activities, andmore research on the association between extreme disasters and firm location choices or firm
migration behaviors is needed to shed more light on the response decisions of firms towards
disasters.
In conclusion, our results suggest that the effects of extreme drought vary across regions
and industries and last a long time. Although the decrease in the economic activities of someindustries might lead to less carbon emissions during extreme disasters, the electricity demand
would still lead to a net increase in carbon emissions. Since there is no efficient substitution
for thermal power in the short term, policies to improve energy efficiency and decrease emission intensity are necessary. The southwestern region is under rapid development and theneed for electricity is growing. Its high dependence on hydropower could mean more potential need for thermal power generation. Local governments might be motivated to develophydropower resources, while extreme weather is a challenging issue for water-intensive power

17

plants. More policies on hydropower generation should be implemented to support their roles
in strengthening energy security and mitigating climate change.

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21

Tables

Table 1 Summary statistics
Control group (1+2)
Variable
Industrial Outcome Data
Output (million yuan)
Revenue (million yuan)
Log output
Log revenue
Firm Characteristics
Age (year)
Employment (person)
Assets (million yuan)Power and Energy-Intensive Sectors
Power and energy-intensive firms
Power firms
Hydropower firms
Thermal power firms
Other power firms
Energy-intensive firm

Treatment group

Mean

Std. dev.

N

Mean

Std. dev.

N

270.641284.572
11.1644.327

2238.749
2504.815
1.357
1.281

593,803
724,475
593,803
724,475

255.032
257.355
11.2024.340

1505.751
1604.025
1.390
1.300

45,11354,534
45,11354,53412.277428.098
233.4249.249
1847.116
3128.143

724,475
684,518
724,493

13.953431.402257.084

11.8311147.841
1713.063

54,544
51,99054,543

0.188
0.020
0.002
0.005
0.00044
0.168

0.391
0.140
0.046
0.073
0.021
0.374

724,548724,548724,548724,548724,548724,548

0.290
0.070
0.028
0.004
0.00006
0.220

0.454
0.255
0.165
0.067
0.007
0.414

54,54854,54854,54854,54854,54854,548Source: NBS Annual Industrial Firm Survey (2006–2013). Note: All monetary variables are deflated to 2015 values using the GDP
deflator (2015=1; World Bank, 2019). We combine the two-digit power industry and energy-intensive industries into the powerand energy-intensive sectors; the power sector contains five four-digit electric power industries, namely the hydroelectric, thermal
electric, nuclear electric, wind, and other electric power industries. Nuclear power firms and wind power firms are combined withother power firms as variable “other power firms” in this study.

22

Table 2 Baseline results
Log output(1)
Drought × Post
× Power and Energy-Intensive
Drought × Post × Energy-Intensive
Drought × Post × Power
Drought × Post × Hydro
Drought × Post × Thermal
Drought × Post × Other
Firm FE
Year FE
Year-province FE
Year-industry FE
Province-industry FE
Observations
Adjust R2

(2)

(3)

Log revenue(4)-0.146** -0.052(0.055) (0.038)

(5)

(6)

(7)

(8)

-0.164*** -0.059*(0.058) (0.031)-0.072** -0.078**(0.034) (0.034)
0.018(0.038)-0.201***(0.019)0.346***(0.047)0.523***(0.022)

no
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
638899 638899 638899
0.222 0.844 0.844

yes
yes
yes
yes
yes
6388990.844-0.070** -0.076**(0.033) (0.033)-0.020(0.030)-0.216***(0.017)-0.088***(0.032)0.213***(0.020)
no
yes
yes
yes
yes
778989
0.223

yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
778989 778989
0.885 0.885

yes
yes
yes
yes
yes
7789890.885

Note: The dependent variables are the logs of output and revenue. Each column presents the results from the DDD model,
estimated by high-dimensional fixed-effects regressions. The constant term is included but not reported. Standard errors are
between parentheses and clustered at the two-digit industry and year-province levels. * p<0.1, ** p<0.05, and *** p<0.01.

23

Table 3 Results without control group 2
Log output(1)
Drought × Post
× Power and Energy-Intensive

(2)

(3)

-0.043(0.033)Drought × Post × Energy-Intensive

(4)

(5)

(6)

-0.059**(0.028)-0.054(0.033)-0.063**(0.028)-0.058**(0.027)-0.055*(0.029)0.003(0.040)Drought × Post × Power
Drought × Post × Hydro-0.061**(0.028)-0.219***(0.017)0.328***(0.047)0.524***(0.026)

Drought × Post × Thermal
Drought × Post × Other
Firm FE
Year FE
Year-province FE
Year-industry FE
Province-industry FE
Observations
Adjust R2

Log revenue

yes
yes
yes
yes
yes
383714
0.843

yes
yes
yes
yes
yes
383714
0.843

yes
yes
yes
yes
yes
3837140.843-0.248***(0.018)-0.122***(0.030)0.199***(0.020)
yes
yes
yes
yes
yes
468697
0.884

yes
yes
yes
yes
yes
468697
0.884

yes
yes
yes
yes
yes
4686970.884

Note: The dependent variables are the logs of output and revenue. Each column presents the results from the DDD model,
estimated by high-dimensional fixed-effects regressions. The constant term is included but not reported. Standard errors are
between parentheses and clustered at the two-digit industry and year-province levels. * p<0.1, ** p<0.05, and *** p<0.01.

24

Table 4 Estimated effects on industrial outcomes by ownership typeLog output

Log revenue

SOE
(1)

COE
(2)

Private Foreign
(3)
(4)

SOE
(5)

COE
(6)

Private Foreign(7)(8)

Drought × Post × Energy-Intensive -0.121*

0.037-0.057 -0.098**-0.104-0.048-0.050-0.131**(0.083) (0.036) (0.040)(0.072)(0.088)(0.034)(0.064)(0.067)
Drought × Post × Hydro-0.220***-0.243*** -0.020 -0.162***-0.225***-0.233***-0.065***-0.138***(0.032)Drought × Post × Thermal
Firm FE
Year FE
Year-province FE
Year-industry FE
Province-industry FE
Observations
Adjust R2(0.051) (0.014) (0.030)(0.028)(0.035)(0.022)(0.046)

0.276*** 0.400*** 0.000 -0.371***-0.122*** 0.245*** 0.264*** -0.365***(0.065)(0.060) (0.000) (0.086)(0.024)(0.075)(0.034)(0.084)

yes
yes
yes
yes
yes
44571
0.910

yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
29038 423323 119318
0.884 0.832 0.868

yes
yes
yes
yes
yes
52927
0.947

yes
yes
yes
yes
yes
33978
0.912

yes
yes
yes
yes
yes
513050
0.872

yes
yes
yes
yes
yes
1510280.905

Note: The dependent variables are the logs of output and revenue. Each column presents the results from the DDD model,
estimated by high-dimensional fixed-effects regressions. The constant term is included but not reported. Standard errors are
between parentheses and clustered at the two-digit industry and year-province levels. * p<0.1, ** p<0.05, and *** p<0.01.

25

Table 5 Sectoral emission intensity by province during 2006–2008
Unit: tCO2 /10000Yuan
year=2006
Thermal power and heating supply sector
Petroleum refineries and coking sector
Industry sector
year=2007
Thermal power and heating supply sector
Petroleum refineries and coking sector
Industry sector
year=2008
Thermal power and heating supply sector
Petroleum refineries and coking sector
Industry sector

Guangxi

Chongqing

Sichuan

Guizhou

Yunnan

11.21
9.31
1.44

14.92
6.58
1.23

14.30
6.50
0.44

19.21
42.83
2.51

18.42
53.60
1.28

9.35
7.77
1.08

14.14
1.95
0.79

9.29
5.54
0.52

17.47
43.50
1.97

17.45
13.80
0.86

6.73
6.59
0.83

10.57
0.79
1.04

7.59
3.25
0.60

13.89
10.99
1.15

11.38
11.73
0.75

Note: Sectoral emission intensity=sectoral CO2 emissions/sectoral output. We use the updated carbon emission factors of raw
coal, crude oil, and natural gas, which are 0.499, 0.838, and 0.590, respectively. Sectoral outputs are deflated to 2015 values using
the GDP deflator (2015=1; World Bank, 2019).

26

Figuresૡ֢ᤒ 12Treatment1-1

Heilongjiang

Inner Mongolia
Jilin

Xinjiang
Beijing
Tianjin

Liaoning

Gansu
Ningxia
Shanxi
Shaanxi

Qinghai

Shandong

Henan
Tibet
Sichuan Chongqing
Guizhou
YunnanTreatment group

JiangsuAnhui
Shanghai

Zhejiang
Hunan Jiangxi
FujianGuangxi
Guangdong

Control group 1
Control group 2

HainanSource: EM-DAT Database, 2006–2013. Note: Provinces in control group 1 and 2 were not affected by the
2009/2010 southwestern drought. However, control group 2 includes provinces affected by another extreme
drought that occurred in 2011, after the southwestern drought. The 2011 drought affected most wheatproducing regions over the middle and lower reaches of the Yangtze River. Water shortages were associatedचԭ ᕪଶ(ኞ౮) ޾ ᕋଶ(ኞ౮) ጱࣈࢶ̶ ᷏ᜋดᐏ Treatment1 ௛ᦇ̶ ຽᦕೲ Province ᬰᤈຽᦕ̶
with the occurrence of both droughts. We use two control groups for the robustness check.

Fig. 1. Treatment and control groups

27

1

.5
0

Estimated effects on revenue

-.5

3
2
1
0

Estimated effects on output-1

Guangxi Chongqing Sichuan
Energy-Intensive

Guizhou

Hydro

YunnanGuangxi Chongqing Sichuan

ThermalEnergy-Intensive

Hydro

YunnanThermal

-.1
-.3
-.5

0

.5

Estimated effects on asset

1

.1

(b)

-.5

Estimated effects on employment(a)Guizhou

Guangxi Chongqing Sichuan
Energy-Intensive

Guizhou

Hydro

YunnanGuangxi Chongqing Sichuan

ThermalEnergy-Intensive(c)Guizhou

Hydro

YunnanThermal(d)Note: The above figures plot the estimated effects of drought on log output, log revenue, log employment, andlog assets across drought-affected provinces. The 95% confidence bands were added as gray solid lines. The
coefficients are obtained by estimating equation (2).

Fig. 2. Effects on industrial outcome by province

28

.4
.2
0
-.4

-.2

Estimated effects on revenue

.2
0
-.2
-.4

Estimated effects on output

-.6

2006

2007

2008

2009
Hydro

2010

2011

2012

2013

2006

2007

2008

Thermal

Hydro

2010

2011

2012

2013

2012

2013

Thermal

(b)

0
-.2
-.4
-.6

-.4

-.2

0

.2

.4

Estimated effects on asset

.6

.2

(a)
Estimated effects on employment

2009

2006

2007

2008

2009
Hydro

2010

2011

2012

2013

Thermal

2006

2007

2008

2009
Hydro

(c)

2010

2011

Thermal(d)Note: The above figures plot the coefficients from estimating equation (2), that is, DDD models with multiple
time periods. Year 2008 is the baseline. Figure (a) shows the annual effects of the southwest drought on thelog output of electric power firms, and the 2010 effect is automatically generated by averaging the effects of2009 and 2011.

Fig. 3. Effects on power sectors over time29

Notes1 The southwestern region includes five provinces and one municipality: Guangxi, Sichuan, Guizhou, Yun-nan, Xizang, and Chongqing.
2 The three most developed economic zones are the Yangtze River Delta Economic Zone, Jingjinji Metropoli-tan Region, and Pearl River Delta Economic Zone.
3 The average output and revenue of the energy-intensive sectors in the treatment group before 2009 were150.6 million yuan and 167.3 million yuan, respectively.
4 The average output and revenue of the hydropower sector in the treatment group before 2009 were 83.8

million yuan and 84.0 million yuan, respectively.
5 The average output and revenue of the thermal power sectors in the treatment group before 2009 were648.7 million yuan and 652.4 million yuan, respectively.
6 Sectoral emission intensity=Sectoral carbon emissions/Sectoral output.

The sectoral output data arecollected from the Statistical Year Book of each southwestern province, and we use the sectoral gross industrial
output for all SOEs and non-SOEs above a designated size.
7 The number of energy-intensive firms is 1,482 and the output loss of each firm is 11.7 million yuan, as

calculated in Subsection 5.2.
8 The number of thermal power firms is 31 and the increase in the output of each firm is 224.5 million yuan,

as calculated in Subsection 5.2.

30

Appendix
Table A1 Power and Energy-Intensive industries classification code
Two-digit industries
25 — Processing of petroleum, coking, processing of nucleus fuel26 — Manufacture of raw chemical material and chemical products
30 (31∗ )— Manufacture of non-metallic mineral products
31 (32∗ ) — Smelting and pressing of ferrous metals
32 (33∗ ) — Smelting and pressing of non-ferrous metals
44 — Production and distribution of electric power and heat power
Four-digit electric power industries
4411 — Thermal power generation
4412 — Hydropower generation
4413 — Nuclear power generation
4414 — Wind power generation
4419 — Other power generation
Source: Statistical Bulletin on National Economic and Social Development in 2010.
31∗ , 32∗ and 33∗ indicate the old CSIC code used in the dataset before 2013.

Table A2 Balancing test results
Variable
Age
Industry
Ownership

Unmatched/Matched Treated mean Control mean %Bias %Reduct bias T-test (P-value)U

12.51110.796

16.4

M

12.511

12.292

2.1

U

26.86427.891-9.8

M

26.86426.797

0.6

U

3.05343.3789-20.9

M

3.05343.0428

0.7

31

0.000
87.2

0.270
0.000

93.5

0.731
0.000

96.8

0.711

Table A3 Estimated effects across drought-hit provinces
e=Energy-Intensive

e=Hydropower

Output Revenue Employment Asset
Guangxi × Post ×Energy e

Sichuan × Post ×Energy e

32

Guizhou × Post ×Energy e

Yunnan × Post ×Energy e(5)Revenue Employment

(1)

(2)

(3)

-0.067-0.040-0.080**-0.059* -0.202*** -0.203***

0.267***(0.043) (0.044)(0.037)(0.032)(0.049)-0.033-0.139**(0.045) (0.059)-0.071*(8)(9)

Revenue Employment

0.105***-0.325***(0.062)(0.027)(0.069)(0.081)(0.037)(0.026)-0.055 -0.139*** -0.166***

0.147-0.095***-0.038-0.0240.513***-0.334***(0.059)(0.042)(0.035)(0.107)(0.020)(0.034)(0.038)(0.083)(0.046)-0.072-0.083**-0.061** -0.156*** -0.233***

0.175***-0.227*** -0.388*** -0.378***-0.210***-0.306***(0.041) (0.044)(0.034)(0.028)(0.022)(0.033)(0.023)(0.120)(0.102)(0.072)(0.048)-0.203 -0.227***-0.256**-0.169** -0.644*** -0.233***-0.005-0.269*** 1.872***

0.065

0.821***-0.217**(0.152) (0.064)(0.103)(0.082)(0.192)(0.076)(0.083)(0.099)(0.084)(0.110)(0.088)-0.077-0.098-0.062-0.036-0.188** -0.184***0.458***-0.223*** 0.725*** 0.261**

0.471***-0.316***(0.069) (0.066)(0.065)(0.050)(0.091)(0.059)(0.100)(0.042)(0.186)(0.115)(0.108)(0.052)(0.020)(0.262)

(10)

Asset

-0.310*** -0.265*** -0.257***(0.036)(7)

Output(12)(0.061)

(6)

Asset

(11)

Chongqing × Post ×Energy e -0.040(4)Outpute=Thermal power

Firm FE

yes

yes

yes

yes

yes

yes

yes

yes

yes

yes

yes

yes

Year FE

yes

yes

yes

yes

yes

yes

yes

yes

yes

yes

yes

yes

Year-province FE

yes

yes

yes

yes

yes

yes

yes

yes

yes

yes

yes

yes

Year-industry FE

yes

yes

yes

yes

yes

yes

yes

yes

yes

yes

yes

yes

Province-industry FE

yes

yes

yes

yes

yes

yes

yes

yes

yes

yes

yes

yes

Observations

638769778837736339778864638769778837736339778864638769778837736339778864

Adjust R2

0.844

0.885

0.815

0.927

0.844

0.885

0.815

0.927

0.844

0.885

0.815

0.927

Note: 2008 is the baseline year. We restrict the sample to observations on common support in this analysis. The dependent variables are the logs of output, revenue, employment, andassets. This table shows the effects of drought on industrial outcomes of the power industries across provinces by the southwestern drought. The constant term is included but notreported. Standard errors are between parentheses and clustered at the two-digit industry and year-province levels. * is p<0.1, ** is p<0.05, and *** is p<0.01.

Table A4 Estimated effects over time
e=Energy-Intensive
Output Revenue Employment
(1)

(2)

Drought ×Energy e × 2006 0.076* 0.079**(3)
0.071***

e=Hydropower
Asset

Output(4)(5)

Revenue Employment
(6)

(7)

0.057*** -0.086**-0.047*0.004

e=Thermal power
Asset

Output(8)(9)

Revenue Employment

Asset

(10)

(11)

(12)

0.182*** -0.556***

0.037-0.361***0.076***(0.042) (0.033)(0.019)(0.016)(0.041)(0.027)(0.030)(0.013)(0.070)(0.067)(0.025)

Drought ×Energy e × 2007 0.023 0.046***

0.048***

0.030**

0.022-0.007-0.016

0.165*** -0.587*** 0.319***-0.477***0.114***(0.024) (0.008)(0.014)(0.013)(0.021)(0.017)(0.032)(0.011)(0.154)(0.033)(0.089)(0.027)-0.031-0.033-0.018**-0.015 -0.142*** -0.160***-0.080***-0.042**0.194** 0.174***-0.033-0.060**(0.022) (0.023)(0.008)(0.018)(0.029)(0.021)(0.020)(0.019)(0.089)(0.035)(0.040)(0.024)0.000-0.020-0.027-0.0170.000-0.169***-0.101***-0.072***0.000

0.100**-0.071***-0.124***(0.000) (0.033)(0.017)(0.018)(0.000)(0.023)(0.028)(0.015)(0.000)(0.041)(0.023)(0.025)-0.034-0.029-0.104***-0.044** -0.214*** -0.281***0.459***-0.164***0.060

0.099**

0.279***-0.218***(0.026) (0.030)(0.030)(0.017)(0.021)(0.034)(0.016)(0.095)(0.039)(0.063)(0.027)-0.060-0.048-0.132***-0.062** -0.216*** -0.258***

0.335***-0.173*** -0.373*** -0.087**-0.140**-0.320***(0.038) (0.030)(0.031)(0.023)(0.024)(0.046)(0.017)(0.036)(0.060)(0.025)-0.054-0.080-0.066 -0.323*** -0.305***

0.622***-0.235*** -0.231** -0.188***0.116*-0.461***(0.046) (0.047)(0.051)(0.043)(0.045)(0.025)(0.030)(0.023)(0.091)(0.054)(0.065)(0.040)Drought ×Energy e × 2009
Drought ×Energy e × 2010
Drought ×Energy e × 201133
Drought ×Energy e × 2012

Drought ×Energy e × 2013 -0.079*(0.027)(0.035)(0.106)(0.086)

Firm FE

yes

yes

yes

yes

yes

yes

yes

yes

yes

yes

yes

yes

Year FE

yes

yes

yes

yes

yes

yes

yes

yes

yes

yes

yes

yes

Year-province FE

yes

yes

yes

yes

yes

yes

yes

yes

yes

yes

yes

yes

Year-industry FE

yes

yes

yes

yes

yes

yes

yes

yes

yes

yes

yes

yes

Province-industry FE

yes

yes

yes

yes

yes

yes

yes

yes

yes

yes

yes

yes

Observations

638769 778837

736339778864638769778837736339778864638769778837736339778864

Adjust R2

0.844

0.815

0.927

0.844

0.885

0.815

0.927

0.844

0.885

0.815

0.927

0.885

Note: 2008 is the baseline year. We restrict the sample to observations on common support in this analysis. The dependent variables are logs of output, revenue, employment,
and assets. This table shows the effects of drought on industrial outcomes of the power industries over time. The constant term is included but not reported. Standard errors are
between parentheses and clustered at the two-digit industry and year-province levels. * is p<0.1, ** is p<0.05, and *** is p<0.01.