influence of environment-conscious management on financial … · 2017. 9. 27. · performance,...

Influence of Environment-conscious Management on Financial Performance: Evidence from

Construction Companies in Japan

Mika Goto, Tokyo Institute of Technology

Yoshito Fukazawa, Tokyo Institute of Technology

Mieko Fujisawa, Kanazawa University

Outline

• Introduction

• Literature review

• Purpose of the study

• Data

• Methodology

• Empirical Results

• Conclusion

2017/9/6 IAEE Europe 2017 @ Wien 2

Introduction (1/3)

• Green growth and sustainable development• Green growth means fostering economic growth and development, while

ensuring that natural assets continue to provide the resources andenvironmental services on which our well-being relies (OECD, 2011: TowardsGreen Growth)

• Green economy• A green economy is one that results in improved human well-being and social

equity, while significantly reducing environmental risks and ecologicalscarcities. In its simplest expression, a green economy can be thought of asone which is low carbon, resource efficient and socially inclusive (UNEP, 2011:Green Economy Report)

• Corporate management for green growth and sustainability


Introduction (2/3)

• Corporate social responsibility (CSR) has been recognized as an essentialcomponent for sustainable operation of firms in modern economy.

• CSR incorporates a wide range of contents, including work style reform,workforce diversity, legal compliance, and environmental protection.

• Environmental protection is one of the most important social issues in theworld, particularly after the enactment of Kyoto Protocol in 2005.

• Many companies have published environmental and sustainability reportsin recent years.• Global Reporting Initiative (GRI) Standards are the first global standards for

sustainability reporting. GRI launched the first version of the guidelines, representingthe first global framework for comprehensive sustainability reporting in 2000.

• Environmental accounting has been introduced by many companies, particularlylisted companies.


Introduction (3/3)

• Concern v.s. Motivation• There is a concern from corporate managers that CSR may impair firms’

profitability.

• Meanwhile, an important motivation for them to introduce theenvironmental accounting is that it may have positive influence on consumersand investors because its introduction gives them good impression onenvironment-conscious management of companies and consequentlyincreases brand and corporate values.


Literature review


Positive

Relationship Examples

Negative

Cochran and Wood (1984), McGuire et al. (1988), Russo and Fouts (1997), Waddock and Graves (1997), Griffin and Mahon (1997), Stanwick and Stanwick (1998), Simpson and Kohers (2002), and Orlitzky et al. (2003).

Mahapatra (1984), Jaggi and Freedman (1992), and Surroca et al. (2010). Moreover, some studies found no relationship between CSR and financial performance, which include Aupperle et al. (1985), Ullman (1985), Pavaand Krausz (1996), and McWilliams and Siegel (2000).

• Margolis and Walsh (2003) surveyed 127 previous studies and found that 70 studies showed a positive relationship, 10 studies presented a negative one, 31 studies did not indicate both positive and negative ones.

• Furthermore, they report that some combinations of these different relationships were found in 23 studies. These mixed results are also observed in studies on Japanese firms.

Purpose of the study

• This study examines the relationship between firms’ efforts forimplementing CSR and their managerial performance using data of 43construction firms in Japan from 2011 to 2015.• Construction companies are one of the key players for the development of

smart cities and communities, and environment-conscious management is animportant factor for sustainable operation of firms.

• This study particularly focuses on the influence from environment-related CSR to financial performance.

• From the results, this study discusses how environment CSR improvesfinancial performances of construction companies in Japan.


Data (1/2)

• This study uses panel data comprising 43 construction companies in Japan over the period 2011–2015.

• CSR data are obtained from Comprehensive CSR Data from Toyo Keizai.

• Financial performance data are from Capital IQ of Standard & Poor’s.

• This study uses four performance measures: • ROA (return on assets)• ROE (return on equity)• TEV (total enterprise value)• EBITDA (earnings before interest, tax, depreciation, and amortization). • ROA and ROE are indexes, and TEV and EBITDA are given in million Japanese

yen.



• We collected 17 items of environment-related CSR as independent variables, andfour financial performance indexes as dependent variables.

• The 17 CSR items are classified into four groups:• (1) Organization and governance for environment protection (Division, Director,

and Percentage),• (2) Environmental accounting (Accounting, Understanding, Cost, Energy,

Greenhouse, and Waste),• (3) Environmental management (Management, CO2, GreenPur, Material,

WaterPol), and• (4) Countermeasures against climate change problem (Climate, Renewable, and

CarbonOff).

Data (2/2)

Method (1/5)

• This study applies a “two-step analysis” using a panel data estimation combined with PCA.


1. This study conducts PCA for CSR data, because 17 items are too many to obtainreasonable results when they are all used in an estimation equation. Thus, weapply PCA and summarize CSR information to essential factors or principalcomponents.

2. This study conducts panel data estimation using the principal components asindependent variables to explain financial measures as dependent variables.

Method (2/5): PCA

• We obtained seven principal components that have eigenvalues more or equal to 1.

• The cumulative proportion of these seven components is approximately 77%.

• The next table describes seven components and eigenvalues that correspond to original17 items as well as classified four groups.

• The results of PCA presents unique characteristics of each component as follows.

• Comp1: Quantitative assessment for environment-conscious corporate management;

• Comp2: Broader effort for environmental accounting and environmental management;

• Comp3: Environmental effort from corporate organization and governance perspectives;

• Comp4: Organization, governance, and cost assessment;

• Comp5: Environmental management and effort for climate change problems;

• Comp6: Organization, governance, and environmental management;

• Comp7: Focused effort for environmental accounting and environmental management.



CSR ItemsMajor

CharacteristicsComp1 Comp2 Comp3 Comp4 Comp5 Comp6 Comp7

Division -0.0745 0.1295 0.0411 -0.2196 -0.2523 0.5488 -0.5096

Director -0.1166 0.2314 0.4966 0.3343 0.0113 0.1029 -0.0203

Percentage -0.0500 0.2822 0.5045 0.3439 0.0991 -0.0199 -0.0426

Accounting 0.0222 0.2903 -0.0412 -0.0083 -0.3896 -0.2851 0.4004

Understanding 0.0737 0.3293 -0.2070 -0.2340 -0.3721 0.1136 0.1187

Cost 0.3399 -0.1873 -0.0777 0.3838 -0.1598 -0.2051 -0.0301

Energy 0.4896 -0.1233 0.1210 0.0534 0.1244 0.0232 0.0472

Greenhouse 0.4904 -0.1023 0.1100 0.0220 -0.0002 0.1883 -0.1137

Waste 0.3278 0.1434 0.2635 -0.3667 0.2332 0.1210 0.1149

Management 0.1165 0.1079 -0.3319 0.2459 0.2767 0.4527 0.3049

CO2 0.1517 0.3159 -0.2900 0.1863 0.2121 -0.0161 0.0757

GreenPur 0.1520 0.2535 0.1793 0.0512 -0.4023 0.2052 0.2861

Material 0.1086 0.3514 -0.0527 -0.0566 -0.0445 -0.4026 -0.3741

WaterPol 0.0453 0.3300 -0.2168 0.1873 0.1498 -0.1317 -0.3781

Climate -0.3083 0.1362 0.1377 -0.2530 0.3822 -0.0595 0.2651

Renewable -0.3136 -0.0596 -0.1621 0.4248 -0.1288 0.2498 0.0151

CarbonOff 0.0483 0.3819 -0.1703 -0.0359 0.2651 0.0907 -0.0231

Organization and

governance

Environmental

accounting

Environmental

management

Countermeasures

against global

warming

Results of principal component analysis

Method (3/5)

Method (4/5)

• In the second step, this study applies panel data estimation.

• The advantage of using a panel data set is that it enables us toexamine the influence from independent variables to a dependentvariable based on rich information in general compared with a case ofcross-sectional data or time series data.

• Further, it captures unobserved unique characteristics of each firm ina constant term. A fixed effects (FE) model and a random effects (RE)model are described in Equations (1) and (2), respectively. The modelselection is conducted by using Hausman test.



Panel data estimation

𝑦𝑖𝑡 = 𝛼𝑖 + 𝒙𝒊𝒕′ 𝜷 + 𝜀𝑖𝑡, (1)

where 𝑦𝑖𝑡 denotes financial performance variables of company 𝑖 𝑖 = 1, … , 𝐼 for period 𝑡 𝑡 = 1,… , 𝑇 ;𝒙𝒊𝒕′ denotes environment-related CSR variables of company 𝑖 𝑖 = 1,… , 𝐼 for period 𝑡 𝑡 = 1,… , 𝑇 ,

which comprises 𝐾 principal components. 𝜷 is a 𝐾 × 1 vector of estimated coefficients, and 𝜀𝑖𝑡 is anerror term with iid [0, 𝜎2]. 𝛼𝑖 is a non-random constant term that captures unobserved company-specificcharacteristics.

𝑦𝑖𝑡 = 𝜇 + 𝒙𝒊𝒕′ 𝜷 + 𝛼𝑖 + 𝜀𝑖𝑡, (2)

The difference of the random-effects model from the fixed-effects model appears in a constant term,which is separated into non-random term 𝜇 and a random term 𝛼𝑖 (iid [0, 𝜎𝛼

2]), which capturesunobserved company-specific characteristics.

All variables except category data are standardized by subtracting averages and divided by standarddeviations when we conduct PCA and panel data estimation.

Method (5/5)

Empirical Results (1/2): panel data estimation


Model

Dependent

Variable

Coefficient t ratio Coefficient t ratio Coefficient t ratio Coefficient t ratio

Constant -0.188 -11.120 *** -0.050 -1.620 -1.497 -2.450 ** -1.631 -2.660 ***

Comp1 0.000 0.010 0.000 -0.020 0.016 0.270 0.031 0.530

Comp2 0.006 3.010 *** 0.002 0.690 0.288 4.680 *** 0.253 4.060 ***

Comp3 0.000 0.150 -0.003 -0.620 0.144 1.200 0.039 0.330

Comp4 0.006 2.810 *** 0.011 2.940 *** 0.021 0.250 0.028 0.350

Comp5 0.006 2.210 ** 0.015 3.050 *** -0.157 -1.460 -0.025 -0.240

Comp6 0.004 1.230 0.014 2.530 ** -0.035 -0.290 0.033 0.280

Comp7 0.001 0.230 0.003 0.480 -0.225 -1.550 -0.098 -0.690

REFE or RE

Model 1 Model 2 Model 3 Model 4

ROA ROE TEV EBITDA

FE FE RE

Note) FE is a fixed effects model, RE is a random effects modelSuperscripts *** and ** indicate statistical significance at the level of 1% and 5%, respectively.

• The most influential one is Comp2 (Broader effort for environmental accountingand environmental management), which is statistically significant for ROA, TEV,and EBITDA at the 1% level.

• The second influential component is Comp4 (Organization, governance, and costassessment), which is significant for ROA and ROE at the 1% level.

• In addition, Comp5 (Environmental management and effort for climate changeproblems) is significant for ROA and ROE at the 5% and 1% levels, respectively.The last one is Comp6, which is significant for ROE at the 5% level.

• It is important to note that all estimated coefficients are positive so that theefforts of firms for introducing and implementing environmental accounting,environmental management, organizational and governance structure forenvironmental protection and cost measurement, and countermeasures againstclimate change problem are all effective to improve financial performance.


Empirical Results (2/2): findings

Conclusion (1/2)

• This study examined the influence of environment-conscious managementof firms on their financial performance using data on 43 Japaneseconstruction companies over the period from 2011 to 2015.

• We used four financial performance measures: ROA, ROE, TEV, and EBITDAas dependent variables. Independent variables were seven principalcomponents that were constructed from 17 environment-related CSRvariables.

• Panel data estimation was employed to examine the relationship betweenthem. The results indicated that broader efforts for environment-consciouscorporate management positively influenced financial performance.

• The results were consistent with previous studies that found positiverelationship between CSR and financial performance.


Conclusion (2/2)

• We have three tasks as an extension of this study.

• (1) Extend industry categories• The extension is important because we expect that the influence of environment-related CSR

on financial performance would vary depending on industries. Such a comparison amongindustries provides us with a new insight on CSR and corporate management.

• (2) Extend CSR items• This study focused on environment-related CSR; however, CSR items are not limited to

environmental issues. Including broader CSR items such as employment, human resourcedevelopment, and corporate governance is another interesting future task of this study.

• (3) Replace a financial performance measure• Firm’s managerial efficiency measure can be obtained from data envelopment analysis (DEA)

and/or stochastic frontier analysis (SFA). They are holistic methods to assess a firm’smanagerial efficiency.


Thank you for your attention.



Year Statistics Division Director Percentage Accounting Understanding Cost Energy Greenhouse Waste

Avg. 2.721 2.023 1.209 2.279 2.442 19,576 1,443,815 240,965 384,352

S.D. 0.549 0.556 0.466 0.959 1.436 59,342 1,503,228 342,334 1,936,012

Min. 1.000 1.000 1.000 1.000 1.000 487 173,759 19,507 330

Max. 3.000 3.000 3.000 3.000 4.000 270,525 5,550,559 1,539,991 10,260,190

Avg. 2.744 1.977 1.256 2.279 2.512 4,621 1,632,654 302,493 396,729

S.D. 0.492 0.556 0.539 0.959 1.470 8,300 1,816,359 426,333 2,001,894

Min. 1.000 1.000 1.000 1.000 1.000 448 205,751 20,932 240

Max. 3.000 3.000 3.000 3.000 4.000 29,835 7,112,144 1,978,055 10,801,300

Avg. 2.744 1.977 1.302 2.302 2.721 59,206 1,878,029 292,848 353,982

S.D. 0.492 0.556 0.558 0.964 1.453 156,989 2,139,478 451,230 1,850,418

Min. 1.000 1.000 1.000 1.000 1.000 424 272,465 26,661 398

Max. 3.000 3.000 3.000 3.000 4.000 629,276 7,822,189 2,172,312 10,321,260

Avg. 2.744 2.000 1.419 2.302 2.767 12,605 1,918,530 269,485 347,235

S.D. 0.492 0.617 0.587 0.964 1.461 28,504 2,099,638 338,018 1,775,467

Min. 1.000 1.000 1.000 1.000 1.000 534 272,079 26,952 402

Max. 3.000 3.000 3.000 3.000 4.000 126,397 7,435,625 1,211,851 9,743,280

Avg. 2.651 1.977 1.395 2.279 2.674 20,279 1,444,732 210,832 407,164

S.D. 0.573 0.597 0.583 0.959 1.459 59,625 1,400,295 239,249 1,941,453

Min. 1.000 1.000 1.000 1.000 1.000 650 263,136 21,907 545

Max. 3.000 3.000 3.000 3.000 4.000 267,212 5,335,265 1,179,770 10,470,460

Avg. 2.721 1.991 1.316 2.288 2.623 23,858 1,670,823 264,259 377,315

S.D. 0.517 0.572 0.549 0.952 1.448 83,472 1,809,051 365,690 1,874,798

Min. 1.000 1.000 1.000 1.000 1.000 424 173,759 19,507 240

Max. 3.000 3.000 3.000 3.000 4.000 629,276 7,822,189 2,172,312 10,801,300

Year Statistics Management CO2 GreenPur Material WaterPol Climate Renewable CarbonOff

Avg. 3.907 2.395 2.047 1.884 2.256 1.605 2.163 1.395

S.D. 0.479 0.903 0.722 0.905 1.157 0.495 0.974 0.695

Min. 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000

Max. 4.000 3.000 3.000 3.000 4.000 2.000 3.000 3.000

Avg. 3.907 2.488 2.070 1.953 2.233 1.651 2.279 1.465

S.D. 0.479 0.856 0.704 0.925 1.130 0.482 0.934 0.767

Min. 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000

Max. 4.000 3.000 3.000 3.000 4.000 2.000 3.000 3.000

Avg. 3.907 2.558 2.047 1.953 2.419 1.698 2.442 1.558

S.D. 0.479 0.825 0.688 0.925 1.200 0.465 0.881 0.825

Min. 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000

Max. 4.000 3.000 3.000 3.000 4.000 2.000 3.000 3.000

Avg. 3.907 2.558 2.000 1.953 2.395 1.744 2.512 1.581

S.D. 0.479 0.825 0.690 0.925 1.218 0.441 0.827 0.823

Min. 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000

Max. 4.000 3.000 3.000 3.000 4.000 2.000 3.000 3.000

Avg. 3.837 2.558 2.000 1.977 2.349 1.767 2.512 1.581

S.D. 0.652 0.825 0.690 0.938 1.213 0.427 0.856 0.823

Min. 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000

Max. 4.000 3.000 3.000 3.000 4.000 2.000 3.000 3.000

Avg. 3.893 2.512 2.033 1.944 2.330 1.693 2.381 1.516

S.D. 0.514 0.842 0.693 0.915 1.175 0.462 0.898 0.784

Min. 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000

Max. 4.000 3.000 3.000 3.000 4.000 2.000 3.000 3.000

2011

2012

2013

2014

2015

Overall

2014

2015

Overall

2011

2012

2013

• We collected 17 items of environment-related CSRas independent variables, and four financialperformance indexes as dependent variables.

• The 17 CSR items are classified into four groups:• (1) Organization and governance for environment

protection (Division, Director, and Percentage),• (2) Environmental accounting (Accounting,

Understanding, Cost, Energy, Greenhouse, andWaste),

• (3) Environmental management (Management, CO2,GreenPur, Material, WaterPol), and

• (4) Countermeasures against climate changeproblem (Climate, Renewable, and CarbonOff).

Descriptive statistics of CSR data


1. Division: w/wo on environment department (3. Yes, 2. Yes but part-time, 1. NA), where w/wo indicates “with” or “without”. The same hereinafter.

2. Director: w/wo on environment officer (3. Yes, 3. Yes but part-time, 1. No or NA),3. Percentage: Share of environment-related business (3. 100%, 2. More than 50%, 1. Less than 50% or NA),4. Accounting: w/wo on environment accounting (3. Yes, 2. Have plan, 1. No or NA),5. Understanding: calculation of cost and benefit from environmental accounting (4. Yes in line with two guidelines from

Environment Ministry and each industry, 3. Yes in line with guideline from Environment Ministry, 2. Yes in line with guideline from each industry, 1. No or NA),

6. Cost (100 thousand Japanese yen): sales/environment-related cost,7. Energy (Japanese yen/ Gigajoule): sales/amount of CO2 emission,8. Greenhouse (100 Japanese yen/ton-CO2); sales/amount of greenhouse gas emissions,9. Waste (Thousand Japanese yen/ton): sales/amount of waste discharges,10. Management: w/wo on environmental management system (4. ISO14001 authentication, 3. Company-specific EMS, 2. Have

plan for ISO14001 authentication, 1. No or NA),11. CO2: w/wo on mid-term plan for CO2 reduction (3. Yes, 2. Under consideration, 1. No or NA),12. GreenPur: Effort for green purchase (3. Conduct green purchase in line with Green Purchasing Network (GPN) guideline, 2.

Conduct green purchase in line with company-–specific guideline, 1. No action or NA),13. Material: Green purchase of materials (3. Conduct under comprehensive guideline, 2. Conduct under partial guideline, 1. No

action or no necessity or NA),14. WaterPol: Status of land and groundwater pollution (4. Measure and publish, 3. Measure but not publish, 2. Limited in partial

measurement, 1. Not measure or NA),15. Climate: Effort for countermeasure against climate change (2. Conduct, 1. No action or NA),16. Renewable: Introduction of renewable energy to business facility or main office (3. Yes, 2. Under consideration, 1. No or NA),17. CarbonOff: Provide products or services with carbon offset (3. Yes, 2. Under consideration, 1. No or NA).


Year Statistics ROA ROE TEV EBITDA

Avg. 0.016 0.008 126761 17784

S.D. 0.016 0.117 227064 30801

Min. -0.036 -0.546 -16777 -6673

Max. 0.054 0.241 852396 158141

Avg. 0.018 0.029 119434 17386

S.D. 0.026 0.126 214897 34560

Min. -0.060 -0.434 -11299 -45131

Max. 0.087 0.306 37686 173766

Avg. 0.029 0.091 182755 23980

S.D. 0.021 0.061 313530 41242

Min. 0.006 0.023 -7443 602

Max. 0.087 0.288 1367484 211459

Avg. 0.031 0.093 216641 27560

S.D. 0.018 0.054 363363 45920

Min. 0.004 0.016 -11356 467

Max. 0.082 0.254 1587899 233317

Avg. 0.041 0.121 260962 38128

S.D. 0.020 0.085 484395 59213

Min. 0.013 0.016 -3239 423

Max. 0.117 0.402 2413865 298657

Avg. 0.027 0.070 181052 25002

S.D. 0.022 0.101 336158 43798

Min. -0.060 -0.546 -16777 -45131

Max. 0.117 0.402 2413865 298657

2011

2012

2013

2014

2015

Overall

Descriptive statistics of financial data

influence of environment-conscious management on financial … · 2017. 9. 27. · performance,...

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