Micro Land Price and Carbon Emission Intensity

doi:10.21203/rs.3.rs-4636149/v1

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Micro Land Price and Carbon Emission Intensity

https://doi.org/10.21203/rs.3.rs-4636149/v1

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This paper investigates the effects and probable mechanisms of micro land price on firm carbon emission intensity in the context of the current globally green and low-carbon transition. Theoretical and empirical research reveal that rising firm land price significantly increase carbon emission intensity across two channels: the financing constraint and the innovation performance. Furthermore, the impact of land price is greater for firms from the central and western regions, high environmental regulation regions, non-state-owned firms, and firms that acquired land through bid invitation, auction and listing. This paper introduces the micro land factor perspective into the field of low carbon development for the first time, providing evidence from developing countries for reducing carbon emission intensity.

JEL：A14; B21; C51; D21

Earth and environmental sciences/Climate sciences/Climate change/Climate change mitigation

Earth and environmental sciences/Climate sciences/Atmospheric science/Atmospheric dynamics

land price

carbon emission intensity

financing constraint

innovation

The increasing production of greenhouse gases (GHG) from human activities has led to a serious climate crisis that is causing damage on the world's security of life and economic development. Global warming is one of the most obvious consequences. Global temperatures will continue to break records throughout the next five years (World Meteorological Organization, 2023). Furthermore, greenhouse gases are causing seawater acidification, glacier melting, and more extreme weather, which damage life and economic development worldwide (the United Nations, 2023). Although there are numerous greenhouse gases, carbon dioxide is widely acknowledged as the most significant emitter of greenhouse gases (Fernando & Hor, 2017). As a result, considerable attempts to limit carbon emissions have been made around the world. To effectively reduce carbon emissions, it is crucial to explore the key factors of carbon emission.

This paper examines the impact of micro land price on carbon emission intensity, as well as the magnitude of the impact and potential mechanisms. The focus on land price in micro level is motivated by three considerations. Firstly, this study could fill a gap in the literature. The production theory often assumes that the land factor is constant, coupled with the lack of micro land transaction data, so there are fewer relevant studies from the perspective of firm land factor. Although the literature finds that land can affect carbon emissions, such as land finance (Wang et al., 2020), price distortion (Gao et al., 2022), mismatch of land resource (Ma et al., 2021), and investment in real estate (Fan & Zhou, 2019) promotes carbon emissions. However, the above studies are mainly discussed at the macro level, it is not clear about the effects and potential mechanisms of land in micro level.

Secondly, the study is anchored in both theory and empirics. As an essential productive input, land influences firms’ choices and activities. Firms will choose the production to maximize profits based on margin theory, and the decision will be influenced by the land price. According to resource allocation theory, land factor resource allocation influences aggregate TFP (Hsieh & Klenow, 2009; Banerjee & Munshi, 2004), R&D investment (Gill & Kharas, 2007), as well as firm entry and exit (Midrigan & Xu, 2014), all of which affect firm carbon emissions (Lee, 2014).

Thirdly, the land issue is a crucial practical matter. Land, particularly in China, is more than simply a production input, but also a strategic tool for government to implement. Land finance accounts for most local government revenues and plays an important role in local economic development. Land finance promotes economic growth (He et al., 2014), expands infrastructure (Zhong et al., 2019; Guo & Shi, 2018), encourages industrialization (Tian, 2015) and urbanization (Ye & Wu, 2014). In addition to fostering the growth of various industries, such as retail (Wassmer, 2002), and real estate (Wang & Hou, 2021; Pan et al., 2015).

In this paper, we first theoretically identify two potential mechanisms of firm land prices affecting carbon emission intensity, including the financing constraint effect and the innovation effect. We construct a general logical analytical framework and propose three hypotheses to be tested. Using the combined five datasets (Annual Survey of Industrial Firms, Pollution of Industrial Firms Database, China Land Transaction Database, Global CO2 Emission Database, and China Patent Database), we test the hypotheses by a two-way fixed-effects model. The findings, which hold up in several robustness tests, indicate that a 1% increase in firm land prices will result in a 0.253% increase in firm carbon emission intensity.

However, the findings are challenged by potential endogeneity problems. This paper adopts two strategies to deal with endogeneity. First, we use the land area ratio as an instrument for land price, and the results from 2SLS shows that a 1% increase in land price will promote a 0.340% increase in carbon emission intensity. Second, we apply the land policy as an exogenous shock to land market and construct an intensity DID model. The findings show that every 1% rise in land price will result in 0.0884% increase in carbon emission intensity. Taken together, the result remains after dealing with the potential endogeneity problem.

The mechanism analysis discovers that for every 1% increase in land price, the financing constraint faced by firms increases by 0.0133% and the level of innovation of firms decreases by 0.0907 percent, confirming the theoretical hypothesis. Furthermore, the effect is stronger for firms in China’s central and western areas (0.278 relative to 0.173), firms in regions with higher levels of environmental regulation (0.329 relative to 0.153), non-state-owned firms (0.297 relative to 0.119), and firms which acquired land through auction (0.323 relative to 0.227).

This paper may make the following contribution to literature. Firstly, it enriches the research related to land and carbon emissions. Existing studies mainly focus on a macro perspective, such as provincial land finance (Wang et al., 2020), the distortion of land price (Gao et al., 2022), mismatch of land resources (Han & Huang, 2022; Du & Li, 2021) on regional carbon emissions. In contrast to macro research perspectives, this paper for the first time explores the micro land perspective.

Secondly, it adds to the literature on impacting factors of carbon emission. Existing literature mainly studies from the spatial and industrial heterogeneity of carbon emissions, including regional economic growth (Waheed et al., 2019), energy structure and industrial structure (Dong et al., 2018), urbanization (Sun & Huang, 2020), and openness (Tamazian et al., 2009; Dietzenbacher et al., 2012). In terms of micro perspectives, these include household-based subject characteristics (Zhang et al., 2015), economic policy uncertainty (Yu et al., 2021), and political affiliation (Wang et al., 2023b). This paper, on the other hand, examines the micro land factor perspective and expands the perspective of carbon emission influencing factors.

Thirdly, it enriches the relevant studies on the impacts of land elements. Except for the study by Yan & Sun (2020), other literature focuses on the connection between land finance and real estate price (Knoll et al., 2017), economic growth (He et al., 2014; Mo, 2018), infrastructure (Zhong et al., 2019; Guo & Shi, 2018), industrialization (Tian, 2015) and urbanization (Ye & Wu, 2014), as well as promoting various industries (Wassmer, 2002; Wang & Hou, 2021; Pan et al., 2015). In addition, the effects of real estate prices, including population migration (Chen et al., 2011; Plantinga et al., 2013), income (Reichert, 1990; Gallin, 2006), employment (Reichert, 1990; Agnew & Lyons, 2018). Distinguishing from the above studies, this paper, on the other hand, examines for the first time the effect of land price on carbon emission intensity, enriching the understanding about land.

The remainders are organized as follows: Section 2 contains a literature review about land finance, carbon emission, and the relationship between them; Section 3 provides a theoretical analysis, which puts forward the hypotheses; Section 4 presents a empirical design that includes data sources, identification equations, variable definitions, and descriptive statistics; Section 5 includes results from baseline regression, as well as check the robustness; Section 6 addresses the endogeneity and presents mechanism analysis; Section 7 provides the heterogeneity analysis; Section 8 concludes and gives policy implications.

2.1 Literature on land finance

The first branch of land finance literature focuses on its roots. Since the 1980s, fiscal decentralization has given local authorities in China room for discretion in how local development operates (Jin & Zou, 2005; Jin et al., 2005). Furthermore, local officials are promoted depending on their contributions to local economic growth (Maskin et al., 2000). Both encouraged local governments to develop land finance aggressively. Together with the two tax reforms in 1994 and 2002, which significantly reduced local governments' share of tax revenues, the central government empowered officials to alleviate their financial problems through land revenues. Since then, land revenues have been essential to local economic growth (Han & Kung, 2015) and have been significantly positively associated with the likelihood of promotion of local officials (Kung & Chen, 2013).

The second strand of literature deals with the impacts caused by land finance on the economy. On the positive side, land finance promotes local economic growth (Mo, 2018; He et al., 2014), the mechanisms include changes in land use (He et al., 2014), the credit quality of local governments (Mo, 2018), the expanding infrastructure (Zhong et al., 2009) and urbanization (Ye & Wu, 2014).

However, researchers started moving their focus to the negative impacts of land finance. It mainly roots in the land mismatch and land factor distortion caused by local government transferring land at very low prices to industrial firms to maximize the land revenue. The effects include distortion in firm production and investment (Neumann, 2001), lower labor productivity (Gai et al. 2017) and total factor productivity (Hsieh & Klenow, 2009), overcapacity (Jiang et al., 2012), overinvestment (Du & Li, 2021), hindered industrial structure upgrading (Han & Huang, 2022), as well as weakened incentive effect of government R&D subsidies on firms (Gill & Kharas, 2007). Moreover, the problem of high housing prices (Wang & Hou, 2021), which is mainly caused by land finance, leading to various barriers to economic development, including inhibiting entrepreneurship (Li & Wu, 2014) and crowding out productive investment (Cull & Xu, 2005; Saint-Paul, 1992).

2.2 Literature on Carbon Emission Intensity

Literature related to carbon emission can be split into two groups. The first studies the spatial, temporal, and inter-industry difference of carbon emission intensity, including cross-country and cross-region differences (Yan et al., 2017), cross-industry differences (Wang et al., 2019; Li & Cheng, 2020), as well as time differences (Gao et al, 2021).

The second category is the various factors affecting carbon emission intensity, such as population size, industrial structure (Wang et al., 2023b), economic development (Waheed et al., 2019), energy structure (Dong et al., 2018), urbanization (Sun & Huang, 2020), openness (Tamazian et al., 2009; Dietzenbacher et al., 2012), the position in global value chain (Sun et al., 2019), foreign direct investment (Demena & Afesorgbor, 2020), and economic policy uncertainty (Yu et al., 2021). At the micro level, firm size (Younis & Sundarakani, 2020), political affiliation (Wang et al., 2023b), and so on.

2.3 Literature on land and carbon emission intensity

Researches on the connection among land and carbon emission intensity put main emphasis on macro perspectives, such as the impact of land finance (Wang et al., 2020), distortions in land prices (Gao et al., 2022), mismatch of land resources (Han & Huang, 2022; Du & Li, 2021 ), land use changes (Lai et al., 2016), urbanization (Sun & Huang, 2020; Zhou et al., 2019), and the real estate industry (Fan & Zhou, 2019; Qashou et al., 2022). In addition, Yu & Zhang (2021) investigates the effect of Low Carbon City Pilot Policy (LCCP), one of carbon-reduction policies in China.

The existing literature gives a rich conceptual framework and empirical evidence for examining the connection among land and carbon emissions, while remains some gaps. They are mainly from a macro perspective, primarily at the provincial level. However, the main source of carbon emissions is industrial firms, which are affected by land in multiple ways. Moreover, most of the existing literature merely explores the causality but lack a systematic mechanism analysis on the decision-making of firms. Therefore, this paper investigates the spatial impact and heterogeneity of land price on carbon emissions, for the first time from the perspective of firms. This paper contributes to a deeper comprehension of the significance of the two's mutual influence.

3.1 Financing constraint effects

For firms, land is a fundamental productive factor, and thus higher land prices lead to greater financial constraints. Li et al. (2023) and Wan et al. (2023) find that financing constraint influences firms' use of fossil fuels, which relates to carbon emission intensity. Specifically, faced with stricter financing constraint, firms are encouraged to reduce other costs. First, using cheaper energy. Industrial firms already tend to use cheaper fossil energy sources, causing more carbon emissions to the environment (Li et al., 2016). Second, reduce R&D expenditures. The financing constraint effect creates higher uncertainty for firms, which delay long-term and risky R&D investments (Bernanke, 1983; Bloom et al., 2007; Kellogg, 2014).

Meanwhile, banks tend to lend more to real estate firms motivated by higher return. Firms are also encouraged to increase their real estate investment, which crowds out other investment, such as R&D investment (Cull & Xu, 2005; Saint-Paul, 1992). Lee & Min (2015) shows the stead link between R&D spending and carbon emission intensity, Accordingly, we put forward the hypothesis1.

Hypothesis 1

Increasing land prices raises carbon emission intensity by exacerbating firm financing constraints.

3.2 Innovation effect

The effect of land price on firms' innovation behavior includes two directions. First, production agglomeration promotes innovation. Land price reflects land values, and high land values imply high levels of agglomeration, including production agglomeration and population agglomeration. Production agglomeration promotes diffusion of technology and encourages firm innovation (Duranton & Puga, 2004), which helps firms improve productivity and energy efficiency (Ciccone & Hall, 1996; Glaeser & Kahn, 2010).

Second, reduced R&D investment inhibits innovation performance. The financing constraint effect in the previous section has shown that higher land prices reduce R&D expenditures, which significantly reduces innovation performance (Ma et al., 2021; Han & Huang, 2022). The reduction of carbon emissions is mostly driven by technological progress. The channels include renewable energy development to optimize the energy structure (Lin & Zhu, 2019), enhancing energy efficiency (Sun et al., 2021), transforming to cleaner production processes (McMeekin et al., 2019). All of them are essential for reducing carbon emission intensity (Lee & Min, 2015). Accordingly, we propose hypothesis 2.

Hypothesis 2

Land price affects carbon emission intensity by influencing firm’s innovation performance, but in an uncertain direction.

Taken together, the overall impact of land price on carbon emission intensity is unclear according to theoretical analysis. Empirical tests are required to further establish the association.

4.1 Data

We use five micro-databases. (1) The Annual Survey of Industrial Firms (ASIF). It is from the China Statistics Bureau, which samples all industrial firms above the scale. In the National Economic Industry Classification (NEIC), industrial firms include extractive industries, manufacturing industries (accounts for more than 90%), as well as electricity, gas, and water industries. This database includes basic and financial information of firms, which has been widely used in the academy. (2) The Pollution of Industrial Firms Database (PIFD). It is aimed at the statistics of pollution emission data of industrial firms above the scale. Solid waste, gas waste, water pollution, and other particular to 27 industrial pollutant emissions, as well as pollution control indicators, are all included in the emissions. It contains the most complete information on the energy consumption and emissions of Chinese industrial firms at present. (3) The China Land Transaction Database. It is from the China Land Market website, which keeps track of data on land transactions across the entire country of China, including detailes on land purchases by firms (land use rights holders). (4) The Global CO2 Emissions Database. It is mainly from the Center for Global Environmental Research (CGER) website, which tracks and provides raster data on global CO2 emissions. (5) The China Patent Database. It is from the State Intellectual Property Office (SIPO), providing detailed information on patent applications by companies.

We match the five datasets based on firm code. Then, we clear and process the data referring to the Brandt et al. (2012). We drop observations with missing data and abnormal data, and match the macro data at the provincial level, which mainly come from the NBS, with the matched firm data. Eventually, we obtain a unique unbalanced panel data spanning from 2000 to 2014, including 12,739 observations. A 99 % winsorization is applied to all continuous variables to avoid outliers from skewing the results.

4.2 Model

To test the hypotheses derived from the previous theoretical frame, we construct a two way fixed effect model referring to the Guo et al. (2023a):

4.3 Variables

Dependent variable (CEI)

Referring to Wang & Wheeler (2003) and He et al. (2020), Carbon Emission Intensity (CEI) is constructed as the ratio of firm carbon dioxide emission over the firm value added, which eliminates the difference in firm size.

Since there is no mandatory requirement in China for firms to disclose information on carbon emissions, the availability of relevant data is low (Pan & Wang, 2022). Referring to Yu et al. (2021), we calculate firm carbon dioxide emissions by multiplying total industry carbon emissions by the firm-to-industry operating cost ratio.

The information on total carbon emissions across industries are obtained from the Carbon Emission Accounts & Datasets (CEADS), which are calculated by using the product of each energy source's consumption and its carbon emission factor, according to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories. The firm operating costs are measured by the sales and marketing cost of goods sold in the main business from the ASIF. Industry operating costs are obtained by adding the operating costs of all firms in the industry. Finally, to facilitate the interpretation, we logarithmize carbon emission intensity to get the dependent variable.

Independent Variable (Price)

Land price (Price) is the variable of our interest. We compute the firm's total transaction price of land bought / the total area of land supply. We crawl the information related to land purchase by firms in the China Land Market website, and then merge it with the ASIF data. Referring to Yan & Sun (2020), if firms transact multiple pieces of land in a year, the average of all transacted land prices is taken. We also logarithmize the land price for simplicity of interpretation.

Figure 1 presents the distribution of firms and their purchased land prices in 2014. We find that land prices faced by firms show a geographical gradient in China, increasing from the west to the east coast.

Control Variables

We control for firm and region characteristics according to Yu et al. (2021), Wang et al. (2023b). As for firm characteristics, we include firm age (Age), size (Size), leverage ratio (Lev), profitability (Profit), and ownership (Soe). Regional control variables include economic growth rate (GdpRate), the share of secondary industry (Structure), population growth (PopGrowth), foreign direct investment (Fdi), government fiscal pressure (FisPressure).

The rationale for the selection of control variables is described below. We choose important firm characteristics. The age of the firm is controlled because firms have different goals and strategies at different growth stages, which may affect carbon emission intensity. Firm size has been shown to affect firm performance (Ozcan et al., 2017), ESG score (Drempetic et al., 2020), as well as carbon emission intensity (Yu et al., 2021). Similarly, leverage ratio and profitability are highly correlated with firms' emission, but the direction of the correlation is unclear. On the one hand, firms must pay to reduce carbon emission which increases costs. On the other hand, reducing emissions may also improve efficiency and reduce costs (Hart & Ahuja, 1996).

The economic development is one of the earliest impacting factors to be identified on carbon emissions, Azomahou et al. (2006) finds a significantly stable correlation between capita GDP and carbon emission intensity. Waheed et al. (2019) reviews the existing research on the link and find that a strand of the studies showing an inverted u-shaped relationship, a line of findings showing a positive correlation, and a school of thought finding no relationship between them.

The economic development is not only about economic growth. The industrial structure also reflects economic prosperity. In fact, changes in industrial structure have been proven to have a fundamental impact on China’s carbon emissions (Green & Stern, 2017). The population growth has also been shown by empirical evidence to affect carbon emissions (Sulaiman & Abdul-Rahim, 2018). Numerous empirical research have also shown a connection among foreign direct investment (FDI) and environmental quality, but there is no consistent conclusion. Eskeland & Harriso (2003) find that foreign-owned firms consume energy which are less polluting and in a more efficient way. In addition, Pazienza (2019) finds a negative link among foreign direct investment and air pollution. And Xing & Kolstad (2002), Acharyya (2009), He (2006) find a positive correlation, which all supporting the pollution haven hypothesis. Apart from the economic development, the fiscal pressure in local government is also an essential factor on firms’ carbon emission intensity. The motivation for government to sacrifice the environment to foster economic growth increases as local governments are put under more financial constraints.

Table 1 shows the definition of variables in this paper.

Table 1. The Definition of Variables

Variable		Definition
Dependent Var.
CEI	Carbon emission intensity	Log(1+Carbon emission/Value added)
Explanatory Var.
Price	Price of land	Log(1+Total price/ Total land supply)
Firm Controls
Age	Age of firm	Log(1+Years of operation since the establishment)
Size	Size of firm	Log(1+ Total asset)
Lev	Asset-liability ratio	Total asset /Total liability
Profit	Profitability	Total Profit/Total asset
Soe	Ownership	SOE=1, non-SOE=0
Region Controls
Gdp	GDP per capita	Log(1+ GDP per capita)
Growth	Development growth	GDP growth rate
Structure	Industry structure	Value added in the secondary industry/value added in the tertiary industry
PopGrowth	Population growth	population growth rate
Fdi	Foreign direct investment	FDI/ GDP
FisPressure	Fiscal pressure	General budget expenditure/general budget income

4.4 Summary Statistics

Table 2 describes the statistics. The average of CEI is 1.72, with the standard deviation 0.986. The logarithm of CEI is about 5.4, which is closer to 3.058 measured by Pan & Wang (2022) using information on the listed companies. Price has an average of 5.14 and a standard deviation of 0.959, which is in line with Yan & Sun (2020)’s finding. Other variables are not described here.

Table 2. The Summary Statistics

Variable	Obs	Mean	Std. Dev.	Min	Max
CEI	12739	1.72	0.986	0.017	7.267
Price	12739	5.14	0.959	0	11.002
Age	12739	2.218	1.282	0	4.428
Size	12739	12.352	1.767	8.618	16.975
Lev	12739	0.404	0.181	0.017	0.868
Profit	12739	0.117	0.182	-0.126	0.959
Soe	12739	0.151	0.358	0	1
Gdp	12739	10.436	0.437	9.354	11.268
Growth	12739	0.142	0.057	0.026	0.259
Structure	12739	1.199	0.217	0.697	1.783
Popgrowth	12739	0.038	0.022	0	0.077
Fdi	12739	0.033	0.019	0	0.067
FisPressure	12739	1.913	0.6	1.118	3.958
Notes: Statistics by the author.

5.1 Baseline Regression

In this part, we empirically explore the causality among land price and carbon emission intensity. Figure 2 shows a positive correlation between land price and carbon emission intensity.

Next, we conduct a rigorous empirical test using the two-way fixed effect Eq. (1). Column (1) of Table 3 presents the result from OLS, showing that a 1% rise in land price for a firm result in a 0.121% increase in the firm's carbon emission intensity.

In column (2), we adopt robust standard errors to overcome possible heteroskedasticity problem. The coefficients and significance remain unchanged, except for a slight increase in the standard errors. In column (3), we add control variables, and the results shows that 1% increase in firm land price will promote a 0.234% increase in firm carbon emission intensity. In Column (4), we control for both firm fixed effects and year fixed effects. The results show that every 1% increase in firm land price leads to firm carbon emission intensity increasing by 0.253%. All the coefficients of Price are significant at the 1% level.

This paper uses the results in Column (4) as a benchmark. We can find that firm land prices significantly contribute to carbon emission intensity. As for the control variables, firm size, industrial structure, and fiscal pressure significantly contribute to carbon emission intensity, which is consistent with existing literature. The results regarding the control variables are not analysed due to space limitations.

Table 3

Baseline Regression Results
	(1)	(2)	(3)	(4)
VARIABLES	CEI	CEI	CEI	CEI
Price	0.121***	0.121***	0.234***	0.253***
	(0.00904)	(0.0103)	(0.0101)	(0.0313)
Age			-0.00926	-0.00897
			(0.00563)	(0.0135)
Size			-0.0118***	0.111*
			(0.00451)	(0.0589)
Lev			0.0771*	-0.161
			(0.0435)	(0.193)
Profit			-0.0607	0.0179
			(0.0420)	(0.189)
Soe			0.0122	-0.0655
			(0.0212)	(0.117)
Gdp			-0.497***	-0.496
			(0.0311)	(0.405)
Growth			1.671***	-2.171**
			(0.154)	(0.953)
Structure			-0.545***	0.608**
			(0.0392)	(0.237)
Popgrowth			-0.0648	0.0878
			(0.325)	(0.786)
Fdi			0.0689	-0.204
			(0.373)	(0.907)
FisPressure			0.535***	0.555***
			(0.0215)	(0.215)
Firm FE	No	No	No	Yes
Year FE	No	No	No	Yes
Robust	No	Yes	Yes	Yes
Observations	12,742	12,742	12,739	12,739
R-squared	0.014	0.014	0.102	0.155
Notes: Firm FE represents firm fixed effects, Year FE represents year fixed effects, and Robust represents adopting clustered robust standard errors. * p < 0.01, p < 0.05, * p < 0.1

5.2 Robustness Test

In this section, we test the robustness. The results are shown in Table 4.

(1) Replacement of dependent variables

Some research uses the ratio of carbon emissions over main business income to measure carbon emission intensity (Chapple et al., 2013). We calculate the CEI in this way and the results are shown in column (1) of Table 4. It shows that every 1% increase in firm land price will promote firm carbon emission intensity by 0.303%, which remains robust.

We also use atmospheric carbon dioxide concentration within 1km of the firm to calculate CEI. The reasons are as follows. First, atmospheric carbon dioxide emissions can be monitored directly rather than calculated from firm data, which are more accurate and valid. Second, carbon dioxide emissions within 1km around the firm are most affected by the firm. Furthermore, the atmospheric carbon dioxide is stable to some extent, while this paper could estimate the effect based on the fluctuation of atmospheric carbon dioxide. The process of calculation is as follows. First, calculate the 1km circular area buffer around the firm. Second, convert the carbon emission data with a resolution of 1km*1km grid provided by CGER into raster data. Third, according to the circular buffer, crop the raster data, and ultimately obtain the carbon emission data within 1km around the firm. The results in Column (2) shows that a 1% rise in firm land price leads to a 0.100% increase in firm’s carbon emission intensity.

Some research also measures carbon emissions directly based on firm energy inputs (Chen, 2009; Pan & Zhang, 2011; Zhang et al., 2011). The calculation formula is as follows.

$$\:{\text{CO}}_{\text{2}}\text{=}\sum\:_{\text{i=1}}^{\text{8}}{\text{E}}_{\text{i}}\text{×}{\text{β}}_{\text{co2,\:i}}\text{=}\sum\:_{\text{i=1}}^{\text{8}}{\text{E}}_{\text{i}}\text{×}\left({\text{NCV}}_{\text{i}}\text{×}{\text{CC}}_{\text{i}}\text{×}{\text{COF}}_{\text{i}}\text{×}\frac{\text{44}}{\text{12}}\right)$$

where $\:{\text{CO}}_{\text{2}}$ represents carbon emissions, and $\:{\text{E}}_{\text{i}}$ shows the total consumption of $\:i$ adjusted to standard coal. We calculate it as total actual consumption of energy*standard coal conversion factor. $\:i$ represents one of the eight major energy sources (coal, coke, crude oil, gasoline, kerosene, diesel fuel, fuel oil, and natural gas). $\:{\text{β}}_{\text{co2,\:i}}$ represents the carbon dioxide emission factor of $\:i$, and we calculate it as $\:{\text{NCV}}_{\text{i}}\text{×}{\text{CC}}_{\text{i}}\text{×}{\text{COF}}_{\text{i}}\text{×}\frac{\text{44}}{\text{12}}$. $\:{\text{NCV}}_{\text{i}}$ is the average low level heat generation of primary energy. $\:{\text{CC}}_{\text{I}}$ is the content of carbon. $\:{\text{COF}}_{\text{I}}$ is the carbon oxidation factor. 44/12 is the ratio of molecular weights among carbon dioxide and carbon.

The information published in PIFD on energy consumption consists of coal, fuel oil, diesel, clean gas, coke and clean gas. However, the information on coke and natural gas are seriously missing. Therefore, we use the consumption of the first four energy, in which the clean gas refers to the conversion factor of natural gas.

We calculate the CEI in the above way and conduct regression. The results are shown in Column (3), indicating that every 1% increase in land price leads to a 0.187% rise in carbon emission intensity, and the results remain robust.

(2) Excluding border firms.

Since firm emissions have negative externalities, the social costs borne by the border areas are much smaller than the economic benefits (Kahn et al., 2015). At the same time, pollution management follows the principle of decentralized territorial management, which leads to local protectionism. It forms environmental protection law enforcement vacuum in the border areas (Gray & Shadbegian, 2017), ultimately leading to serious pollution in the border areas.

Therefore, the existence of border firms may overestimate the magnitude of the effect, so we delete the firms located in the provincial borders to re-regress. Column (4) shows that a 1% increase in land price increase carbon emission intensity by 0.149%, and the results remain robust.

(3) removing the impact of carbon pilot policy

In 2010, the National Development and Reform Commission promulgated a pilot policy for low-carbon cities to effectively control greenhouse gas emissions, increasing gradually the number of pilot cities and broadening the policy influence. We restrict the sample spanning from 2000 to 2009, to exclude the effect of the policy. The results in Column (5) shows that a 1% increase in land price leads to a 0.216% rise in carbon emission intensity, and the results remain robust.

(4) DK standard errors

To address the potential autocorrelation, heteroskedasticity and cross-section correlation, the DK standard error (Driscoll & Kraay,1998) is used. Column (6) shows that every 1% rise in land price results in a 0.240% in carbon emission intensity, and the results remain robust.

Table 4

Robustness
	(1)	(2)	(3)	(4)	(5)	(6)
VARIABLES	CEI	CEI	CEI	CEI	CEI	CEI
Price	0.303***	0.100***	0.187**	0.149***	0.216**	0.240***
	(0.0412)	(0.00159)	(0.0816)	(0.0463)	(0.0965)	(0.0239)
Controls	Yes	Yes	Yes	Yes	Yes	Yes
Firm FE	Yes	Yes	Yes	Yes	Yes	Yes
Year FE	Yes	Yes	Yes	Yes	Yes	Yes
Robust	Yes	Yes	Yes	Yes	Yes	Yes
Observations	12,729	12,731	445	5,611	3,215	12,742
R-squared	0.188	0.718	0.213	0.169	0.208	0.139
Notes: Firm FE represents firm fixed effects, Year FE represents year fixed effects, and Robust represents adopting clustered robust standard errors. * p < 0.01, p < 0.05, * p < 0.1

In the section above, we construct a two-way fixed effects model for causal identification. However, this identification strategy still faces the challenge of potential endogeneity. For example, a rise in firm land price increases carbon emission intensity, but an increase in carbon emission intensity can also raise land price. Because higher carbon emission intensity is accompanied by stricter government regulation (He & Liu, 2018; Liu & Mu, 2016; Yu et al., 2023), leading to firms' difficulties in acquiring land and a rise in land prices, which is a typical reverse causality problem. Then again, endogeneity arising from measurement errors and unobservable in land prices can also make the coefficients biased.

To overcome the above potential endogeneity concerns, this section will adopt two empirical strategies. In addition, this section also tests the potential mechanisms, and the results are shown in Table 5.

6.1 Instrument

Referring to Yan & Sun (2020), we instrument the land price by land volume ratio. First, the land volume ratio is exogenous, because it is decided by the government before the transaction and it has no direct effect on firms’ carbon emission intensity. Second, there is a stably and significantly positive connection among the land volume ratio and land price. The measurement equation of the two-stage instrumental variable method is as follows:

6.2 Land policy shocks

The Notice on Further Strengthening the Management of Land Transfer Revenue and Expenditure (MLTRE) was issued in 2010. The notice requires that land transfer revenues be paid in full to the local treasury, while expenditures are made from land transfer revenues through local fund budgets. Local governments will be held administratively responsible if they fail to pay land transfer revenues in full and on time, or overstep their authority to reduce or slow down the payment of land transfer revenues, or reduce land transfer revenues in disguise, etc.

After the MLTRE is put into effect, the government's land finance is greatly restrained and the supply of land to firms is reduced, which is undoubtedly an exogenous shock to land price. The policy provides us with a quasi-natural experiment to identify the causality between land price and the carbon emission intensity. Considering that firms with higher land acquisition before the policy is enacted will be hit harder, referring to Lu and Yu (2015), the following intensity DID equation is constructed.

6.3 Mechanism Analysis

In Column (4) and Column (5), we test the mechanism hypotheses. According to the hypothesis, it is expected that the land price can exacerbate the firm financing constraints. Referring to Yan & Sun (2020), we calculate the firm's financing constraint (Constraint) as total liabilities/total assets. The results shown in Column (4) indicates that the land price significantly exacerbates the financing constraints of firms, which is in line with expectations. According to hypothesis 2, it is unclear about the effect of land price on firm’s innovation performance. We measure firm innovation performance (Innovation) by the quantity of patent applications, due to its timely and accurate reflection (Li & Zheng, 2016). We obtain the data from the patent application database of the State Intellectual Property Office of China, referring to He et al. (2018). The results in Column (5) shows that the land price significantly inhibits the level of firm innovation.

Table 5. Results from 2SLS and Mechanism Analysis

	2SLS			Mechanisms
VARIABLES	Price	CEI	CEI	Constraint	Innovation
	(1)	(2)	(3)	(4)	(5)
Price		0.340***		0.0133***	-0.0907***
		(0.0960)		(0.00275)	(0.00718)
Land#Post			0.0884***
			(0.0255)
Volume	0.942***
	(0.186)
Controls	Yes	Yes	Yes	Yes	Yes
Firm FE	Yes	Yes	Yes	Yes	Yes
Year FE	Yes	Yes	Yes	Yes	Yes
Robust	Yes	Yes	Yes	Yes	Yes
Observations	2,741	2,741	12,739	12,399	12,739
R-squared	0.160	0.161	0.091	0.493	0.096

Notes: The Kleibergen-Paap rk LM statistic is 33.498, which significantly rejects the null hypothesis of under-identification test. The hypothesis of weak instrumental variables is rejected, because Kleibergen-Paap rk Wald F statistic is 25.806. Firm FE represents firm fixed effects, Year FE represents year fixed effects, and Robust represents adopting clustered robust standard errors. *** p<0.01, ** p<0.05, * p<0.1

In this section, we launch the heterogeneity analyses. The results are shown in Table 6.

7.1 Region

China is massive with considerable regional variations, whether in terms of economic growth or institutional environment (Gao et al., 2022). As a result, there is clear regional variability in how land factors affect environmental performance (Gao et al., 2022; Wang et al., 2022). In the central and western regions, it is anticipated that the effect of land price will be greater (Wang et al., 2023b). Because the concentration of energy-intensive industries, lack of green innovation, and tendency of the government and firms to sacrifice the environment to pursue profits. We split the sample into two groups, the eastern and the mid-western, according to the standard of the National Bureau of Statistics. Columns (1)-(2) show that the impact is larger in the mid-western regions (0.278 > 0.173), as expected.

7.2 Type of land transfer

There are two main ways of land granting in China: (1) agreement; (2) bid invitation, auction and listing (abbreviated as BAL). According to Yan & Sun (2020), the land price in the agreement transferring is lower than that in the other one, so the effect of land price on carbon emission intensity of firms which bought land in BAL transferring may be lower in the agreement transferring. We divide the sample into two groups based on the land transfer methods and regress separately. Columns (3)-(4) show that the coefficient of Price is larger (0.323 > 0.227) in the way of BAL, as expected.

7.3 Ownership

In China, state-owned firms (SOEs) have specific strategic position and government relations, making it easier to obtain political incentives and financial support than non-SOEs (Tang et al., 2020). Therefore, SOEs face a soft budget constraint, which implies that SOEs are likely to be less affected by land prices (Yan & Sun, 2020). It is reasonable to expect the effect to be lower in SOEs. According to the type of firm registration in ASIF, the sample is divided into two subsamples of SOEs and non-SOEs and regressed separately. Columns (5)-(6) show that the impact coefficient of Price is larger in non-SOEs (0.297 > 0.119), as expected. The result is also consistent with the above findings on land granting methods, which shows that SOEs tend to be able to acquire land at agreed low prices due to their natural closeness to government departments.

7.4 Regulation

The stronger penalties are levied on firms in regions with more severe environmental regulations (Wang et al., 2023a). Then the greater the incentive for firms in the region to improve their environmental performance (Guo et al., 2023b). Therefore, it is reasonable to assume that the effect of land price is greater for firms that faced relaxed environmental regulations. Using each province’s information on three emissions, we compute the composite index of environmental regulation by entropy approach. Based on the median of this index, firms are grouped into two subsamples. Columns (7)-(8) show that the impact coefficient of Price is larger at the low environmental regulation level (0.329 > 0.153), as expected.

Table 6

Heterogeneity analysis
	Region		Transfer		Ownership		Regulation
	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)
VARIABLES	East	West & Central	Aggrement	BAL	Soe	Non-Soe	High	Low
price1	0.173**	0.278***	0.227***	0.323***	0.119*	0.297***	0.153***	0.329***
	(0.0709)	(0.0505)	(0.0851)	(0.0452)	(0.0693)	(0.0362)	(0.0461)	(0.0581)
Controls	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Y
Firm FE	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Y
Year FE	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Y
Robust	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Y
Observations	4,606	8,133	2,488	10,251	1,922	10,817	5,573	7,166
R-squared	0.314	0.159	0.391	0.161	0.137	0.173	0.140	0.213
Notes: Firm FE represents firm fixed effects, Year FE represents year fixed effects, and Robust represents adopting clustered robust standard errors. * p < 0.01, p < 0.05, * p < 0.1

China has proposed carbon peaking and carbon neutrality objectives. This paper investigates how the land price affect firm carbon emission for the first time. The findings show that rising land prices significantly increase firm carbon emission intensity, and the underlying mechanisms are exacerbating firm financing constraints and inhibiting firm innovation. In particular, the impact is more pronounced for firms not from eastern regions, those that are located in areas with less environmental controls, firms that are not state-owned, and firms that obtain land through auction and listing.

We can draw some policy implications from our findings. First, promote the reform of land factor market. By establishing a clear property right in the land market, we can weaken local governments' monopolies and transition from the current policy-based pricing to market-based pricing system. The transition will form an effective competitive pattern in the land market (Zeng et al., 2022; Gao et al., 2022), avoiding the rapid increase in land prices. Second, alleviate firm financing constraints. It is necessary to ensure that industrial firms have access to sufficient capital, to reduce the firm financing constraints imposed by rising land prices. For example, further promote financial inclusion. Third, promote firm innovation. The government can not only increase R&D subsidies to firms (Guo et al., 2016), but also promote innovative public research. At the same time, it should not be ignored that the legal environment and government efficiency (Jiao et al., 2015). Government can promote the establishment of intellectual property rights (IPR) protection system. Fourth, improve environmental regulation policies. The government could further strengthen environmental regulations and push firms to follow. The formulation process should go through based on regional and industrial characteristic, and be adjusted over time (Porter & Linde, 1995). Environmental taxes, emissions quota trading (Stewart, 1993) and tax-subsidy mechanisms can be flexibly applied as environmental regulatory instruments (Jaffe & Stavins, 1995; Zhang et al., 2011).

However, there are some shortcomings in this paper. How to include mechanisms into a local equilibrium framework, explain the connection among micro land price and carbon emission intensity, and depict the amount and direction of the prospective action mechanism's influence? This is an essential future research direction.

Conflict of Interest

The Authors declare that there is no conflict of interest.

Funding Statement

The research was funded by the Doctoral Research Innovation Program of Weifang Medical University (Grant No. 041174).

Ethics Statement

There are no human subjects in this article.

Data availability statement

The datasets used and analyzed during the current study available from the corresponding author on reasonable request.

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See the National Bureau of Statistics website (stats.gov.cn).

No competing interests reported.

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Micro Land Price and Carbon Emission Intensity

Status:

Version 1

Abstract

Figures

1 Introduction

2 Literature Review

2.1 Literature on land finance

2.2 Literature on Carbon Emission Intensity

2.3 Literature on land and carbon emission intensity

3 Theoretical Frame

3.1 Financing constraint effects

3.2 Innovation effect

4 Empirical Design

4.1 Data

4.2 Model

4.3 Variables

4.4 Summary Statistics

5 Empirical Results

5.1 Baseline Regression

5.2 Robustness Test

6 Discussion

6.1 Instrument

6.2 Land policy shocks

6.3 Mechanism Analysis

7 Heterogeneity

7.1 Region

7.2 Type of land transfer

7.3 Ownership

7.4 Regulation

8 Conclusion and policy implications

Declarations

References

Footnotes

Additional Declarations

Status:

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