Influence of Environment-conscious Management on Financial - - PowerPoint PPT Presentation

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Influence of Environment-conscious Management

• n Financial Performance: Evidence from

Construction Companies in Japan

Mika Goto, Tokyo Institute of Technology Yoshito Fukazawa, Tokyo Institute of Technology Mieko Fujisawa, Kanazawa University

SLIDE 2

Outline

• Introduction
• Literature review
• Purpose of the study
• Data
• Methodology
• Empirical Results
• Conclusion

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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 and environmental services on which our well-being relies (OECD, 2011: Towards Green Growth)

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

equity, while significantly reducing environmental risks and ecological

• scarcities. In its simplest expression, a green economy can be thought of as
• ne which is low carbon, resource efficient and socially inclusive (UNEP, 2011:

Green Economy Report)

• Corporate management for green growth and sustainability

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Introduction (2/3)

• Corporate social responsibility (CSR) has been recognized as an essential

component 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 the

world, particularly after the enactment of Kyoto Protocol in 2005.

• Many companies have published environmental and sustainability reports

in recent years.

• Global Reporting Initiative (GRI) Standards are the first global standards for

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

• Environmental accounting has been introduced by many companies, particularly

listed companies.

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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 the environmental accounting is that it may have positive influence on consumers and investors because its introduction gives them good impression on environment-conscious management

• f

companies and consequently increases brand and corporate values.

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Literature review

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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), Pava and 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.

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Purpose of the study

• This study examines the relationship between firms’ efforts for

implementing CSR and their managerial performance using data of 43 construction 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 an important 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 improves

financial performances of construction companies in Japan.

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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.

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• We collected 17 items of environment-related CSR as independent variables, and

four 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)

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Method (1/5)

• This study applies a “two-step analysis” using a panel data estimation

combined with PCA.

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• 1. This study conducts PCA for CSR data, because 17 items are too many to obtain

reasonable results when they are all used in an estimation equation. Thus, we apply PCA and summarize CSR information to essential factors or principal components.

• 2. This study conducts panel data estimation using the principal components as

independent variables to explain financial measures as dependent variables.

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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 original

17 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.

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2017/9/6 IAEE Europe 2017 @ Wien 12 CSR Items Major Characteristics Comp1 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)

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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 to

examine the influence from independent variables to a dependent variable based on rich information in general compared with a case of cross-sectional data or time series data.

• Further, it captures unobserved unique characteristics of each firm in

a constant term. A fixed effects (FE) model and a random effects (RE) model are described in Equations (1) and (2), respectively. The model selection is conducted by using Hausman test.

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Panel data estimation

𝑧𝑗𝑢 = 𝛽𝑗 + 𝒚𝒋𝒖

′ 𝜸 + 𝜁𝑗𝑢,

(1) where 𝑧𝑗𝑢 denotes financial performance variables of company 𝑗 𝑗 = 1, … , 𝐽 for period 𝑢 𝑢 = 1, … , 𝑈 ; 𝒚𝒋𝒖

′ denotes environment-related CSR variables of company 𝑗 𝑗 = 1, … , 𝐽

for period 𝑢 𝑢 = 1, … , 𝑈 , which comprises 𝐿 principal components. 𝜸 is a 𝐿 × 1 vector of estimated coefficients, and 𝜁𝑗𝑢 is an error term with iid [0, 𝜏2]. 𝛽𝑗 is a non-random constant term that captures unobserved company-specific characteristics. 𝑧𝑗𝑢 = 𝜈 + 𝒚𝒋𝒖

′ 𝜸 + 𝛽𝑗 + 𝜁𝑗𝑢,

(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 𝜈 and a random term 𝛽𝑗 (iid [0, 𝜏𝛽

2]), which captures

unobserved company-specific characteristics. All variables except category data are standardized by subtracting averages and divided by standard deviations when we conduct PCA and panel data estimation.

Method (5/5)

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Empirical Results (1/2): panel data estimation

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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

RE FE 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 model Superscripts *** and ** indicate statistical significance at the level of 1% and 5%, respectively.

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• The most influential one is Comp2 (Broader effort for environmental accounting

and environmental management), which is statistically significant for ROA, TEV, and EBITDA at the 1% level.

• The second influential component is Comp4 (Organization, governance, and cost

assessment), which is significant for ROA and ROE at the 1% level.

• In addition, Comp5 (Environmental management and effort for climate change

problems) 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 the

efforts of firms for introducing and implementing environmental accounting, environmental management, organizational and governance structure for environmental protection and cost measurement, and countermeasures against climate change problem are all effective to improve financial performance.

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Empirical Results (2/2): findings

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Conclusion (1/2)

• This study examined the influence of environment-conscious management
• f firms on their financial performance using data on 43 Japanese

construction companies over the period from 2011 to 2015.

• We used four financial performance measures: ROA, ROE, TEV, and EBITDA

as dependent variables. Independent variables were seven principal components that were constructed from 17 environment-related CSR variables.

• Panel data estimation was employed to examine the relationship between
• them. The results indicated that broader efforts for environment-conscious

corporate management positively influenced financial performance.

• The results were consistent with previous studies that found positive

relationship between CSR and financial performance.

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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
• n financial performance would vary depending on industries. Such a comparison among

industries 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 resource development, 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’s managerial efficiency.

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Thank you for your attention.

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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 CSR

as independent variables, and four 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). Descriptive statistics of CSR data

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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).

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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