SWOT and TOWS matrixes for EMS
\\n\\n
More than half of the publishers listed alongside IntechOpen (18 out of 30) are Social Science and Humanities publishers. IntechOpen is an exception to this as a leader in not only Open Access content but Open Access content across all scientific disciplines, including Physical Sciences, Engineering and Technology, Health Sciences, Life Science, and Social Sciences and Humanities.
\\n\\nOur breakdown of titles published demonstrates this with 47% PET, 31% HS, 18% LS, and 4% SSH books published.
\\n\\n“Even though ItechOpen has shown the potential of sci-tech books using an OA approach,” other publishers “have shown little interest in OA books.”
\\n\\nAdditionally, each book published by IntechOpen contains original content and research findings.
\\n\\nWe are honored to be among such prestigious publishers and we hope to continue to spearhead that growth in our quest to promote Open Access as a true pioneer in OA book publishing.
\\n\\n\\n\\n
\\n"}]',published:!0,mainMedia:null},components:[{type:"htmlEditorComponent",content:'
Simba Information has released its Open Access Book Publishing 2020 - 2024 report and has again identified IntechOpen as the world’s largest Open Access book publisher by title count.
\n\nSimba Information is a leading provider for market intelligence and forecasts in the media and publishing industry. The report, published every year, provides an overview and financial outlook for the global professional e-book publishing market.
\n\nIntechOpen, De Gruyter, and Frontiers are the largest OA book publishers by title count, with IntechOpen coming in at first place with 5,101 OA books published, a good 1,782 titles ahead of the nearest competitor.
\n\nSince the first Open Access Book Publishing report published in 2016, IntechOpen has held the top stop each year.
\n\n\n\nMore than half of the publishers listed alongside IntechOpen (18 out of 30) are Social Science and Humanities publishers. IntechOpen is an exception to this as a leader in not only Open Access content but Open Access content across all scientific disciplines, including Physical Sciences, Engineering and Technology, Health Sciences, Life Science, and Social Sciences and Humanities.
\n\nOur breakdown of titles published demonstrates this with 47% PET, 31% HS, 18% LS, and 4% SSH books published.
\n\n“Even though ItechOpen has shown the potential of sci-tech books using an OA approach,” other publishers “have shown little interest in OA books.”
\n\nAdditionally, each book published by IntechOpen contains original content and research findings.
\n\nWe are honored to be among such prestigious publishers and we hope to continue to spearhead that growth in our quest to promote Open Access as a true pioneer in OA book publishing.
\n\n\n\n
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Environmental management has quickly emerged as an essential strategic factor in many industries. Environmental considerations are clearly becoming increasingly important and will be considered as one of the key factors in most companies’ success stories. For example, recently there are many firms in Asia that had already received ISO 14001 certification and adopted these Environmental Management Systems (EMS) standards as their state policy. No doubt that many firms have recognized the compatibility between environmental performance and profitability, as it witnessed by increasing interest in recycling programs and green marketing, in part due to realizing that the futility of running from such pressures.
Melnyk et al. (2003) apply a survey of North American managers to demonstrate that firms having gone through EMS certification experience a greater impact on performance than do firms that have not certified their EMS. Pan (2003) applies questionnaires to the organizations within Taiwan, Japan, Hong Kong and Korea on regards of ISO9000 and ISO14000 issues. He uses statistical analysis results of the survey data to gain eight common points for ISO9000 and ISO14000 certified firm within these four countries. Tan et al. (2003) develop an e-commerce structure for sorting, selecting and utilizing information for the effect of ISO9000 system. The related studies of environmental issues will be listed in Environment Management (Ahsen and Funck, 2001; Rao et al., 2006; Gernuks et al., 2007), Environmental Management Accounting (Jasch, 2003), ISO14001 Certification (Fryxell and Szteo, 2002; Mbohwa and Fukada, 2002; Rennings et al., 2006) and Life Cycle Assessment for EMS (Zobel, 2002).
In a country’s endeavor to implement EMS in both manufacturing and service sectors, the significance of Small and Medium Enterprises (SMEs) deserves special attention. In Taiwan, a SME is set under either two conditions. First, it is defined by the number of employees that they often refer to those with less than 200 employees involved in manufacturing, building and mining industries. Second, it is defined by its capital volume that is less than 80 million Taiwan dollars. The SMEs are typically much smaller in operation compared to the global and multinational enterprises, whereas most of the SEMs in Taiwan are positioned in the ending-role of the supply chain. Most EMSs in Taiwanese SMEs are implemented in accordance to specification in ISO 14001 or QC080000 standards, in which contain requirements that have to be fulfilled before third-party certification and /or registration can be achieved.
Strength, Weakness, Opportunity and Threat (SWOT) analysis is an important support tool for decision-making, and is commonly used as a means to systematically analyze an organization’s internal management capability and its external environment. The purpose of the analysis on internal strengths and weaknesses is to assess how an enterprise carries out its internal work, such as R&D, day to day business operation, etc. On the other hand, the purpose of the analysis on the external opportunities and threats is to assess whether or not an enterprise can seize opportunities and avoid threats, whilst facing an uncontrollable external environment, such as fluctuating prices, political destabilization, etc. SWOT analysis has been successfully applied in EMS fields, such as the environmental impact assessment in India (Paliwal, 2006), the development of an environmental management system (Lozano and Vallés, 2007) and regional energy planning for renewable development (Terrados et al., 2007).
For a quantitative SWOT, Kuttila et al. (2000) develop a hybrid method, the Analysis Hierarchy Process (AHP) in the SWOT analysis, to eliminate the weakness in the measurement and evaluation steps of the SWOT analysis. Examples in literature of studies that follow the method of Kuttila et al. include those by Kajanus et al. (2004), Leskinen et al. (2006) and Chang and Huang (2006). Yüksel and Dağdeviren (2007) demonstrate a process for quantitative SWOT analysis that can be performed even when there is dependence among strategic factors. They use the Analytic Network Process (ANP) that allows measurement of the dependency among the strategic factors as well as its AHP, which is based on independence between the factors. ANP is a more general form of its predecessor, the AHP, for ranking alternatives based on some set of criteria. Unlike AHP however, ANP is capable of handling feedbacks and interdependencies, which exist, in complex systems like a manufacturing system. ANP problem formulation starts by modeling the problem that depicts the dependence and influences of the factors involved to the goal or higher-level performance objectives. Dependence among the SWOT factors is observed to effect the strategic and sub-factor weights, as well as to change the strategy priorities. Dyson (2004) provides an SWOT and TOWS analysis to create strategy formation and its incorporation into the strategic development process at University of Warwick by scoring SWOT factors. A variation of SWOT analysis is the TOWS matrix. In the TOWS matrix the various factors are identified and these are then paired e.g. an opportunity with a strength, with the intention of stimulating a new strategic initiatives (Table 1).
A “top-down” way of thinking could be used to guide the formulation of decision hierarchy. In this paper, a new quantitative SWOT analysis is provided that allows measurement of the strategic factors as well as its vote-ranking method. The first task is to invite eighteen certificated ISO9000 and ISO14000 auditors (or lead auditors) to organize a “Task Force (TF)”. The TF will discuss SWOT of Taiwanese SMEs within their EMS issues and assess the competitive strategies. The second task is to apply the internal competitive strengths to find external market opportunities. As a result, the strategy combination for max {strengths, opportunities} and min {weaknesses, threats} will be provided. The third task is to regard these SWOT indexes and their sub-criteria as the candidates voted by the task force. In conclusion, the different results of ranking will expose different weights among the votes of the candidates.
This paper discusses the environmental issues of the SMEs not only by drawing insights from research conducted in different countries, but also look into the use of environmental factors of SWOT through their development, their context and adaptability to enhance the environment performance of SMEs. As for the medium, the vote-ranking method will be used to rank the different competitive strategies and priorities. This specific method provides a new quantitative SWOT methodology that will be extended to decision-making issues. The rest of this paper is organized as follows: Section 2 illustrates the vote-ranking method and the conceptual approach. Section 3 discloses the use of vote-ranking method to provide a quantitative SWOT method for assessing the SMEs’ competitive strategies in EMS by six-step procedure. Section 4 discusses the results of different strategies and suggestions. Section 5 highlights some conclusions and offers directions for further researches.
Data Envelopment Analysis (DEA) is an analytical procedure developed by Charnes et al. (1978) for measuring the relative efficiency of decision-making units (DMUs) that perform the same types of functions and have identical goals and objectives. The weights used for each DMU are those which maximize the ratio between the weighted output and weighted input. DEA is a mathematical programming technique that calculates the relative efficiencies of multiple DMUs, based on multiple inputs and outputs. A well-known method for ranking candidates in a ranked voting system is to compare the weighted sum of their votes after determining suitable weights. Cook and Kress (1990, 1992) present an approach to the problem of ranking candidates in a preferential election. They consider an alternative method which does not specify the sequence of weights by applying DEA. One would imagine that any reasonable person, voter, candidate or poll manager would agree that the first place votes should weigh at least as much as second place votes, and so on. They provide the following DEA model to obtain the total score for each candidate:
Where,
d(
The Cook and Kress’s ranked voting model (1) is assumed that in a voting system, each voter selects
Green et al. (1996) further develop this model by setting certain constraints to the weights. They point out that the form of d(s, ε) affects the ranking results and does not allow DMUs to choose their own weights unreservedly. Therefore, they present an alternative procedure that involved using each candidate’s rating of him/herself along with each candidate’s rating of all the other candidates. They utilize the cross-efficiency model to DEA to obtain the best candidate. On the other hand, Hashimoto (1997) proposes a method to determine a total ordering of candidates specifying nothing arbitrary, but only assuming the condition of decreasing and convex sequence of weights. They incorporate the condition of decreasing and convex sequence of weights into DEA as the assurance region. Green et al. and Hashimoto proposes these methods, whereabouts the existence of low preference candidates may change the ranks and DEA exclusion model, which seems to be unstable with respect to the inefficient candidates. Obata and Ishii (2003) consider that, the instability is caused by the fact mentioned above and that the inefficient candidates should not be used to discriminate efficient candidates. They also use this information only on efficient candidate while discriminating and realizing that the order of efficient candidates never changes even though the inefficient candidates are added or removed. Foroughi and Tamiz (2005) simplify the model of Obata and Ishii and extend it to rank the inefficient candidates as well as the efficient one.
Noguchi et al. (2002) revise the application of Green’s method and show that different weights among objects gave rise to different ranking results. If one wants to set particular constraints to a weight can be employed, which is characterized by the following constraints: (a)
Where, these variables are the identical as model (1).
As for ranking of alternatives, one of the most popular methods compares the weighted sum of votes after determining suitable weights for each alternative. The different weights among objects resulted in different ranking results and propose a new method of ordering in order to solve the problem of weights ranking. As a final point, the module solver imbedded in EXCEL of Microsoft Office [2003] will be applied to solve the above linear programming problems (Liu and Hai, 2005).
This study proposes six-step procedure for selecting the competitive strategies of the Taiwanese SMEs. They are obtained from TF which will fall into four subjective criteria that discuss and analyze SWOT of Taiwan’s SMEs in the EMS. The first step is structuring the problem into a SWOT hierarchy. On the top level is the overall goal of selection competitive strategies. On the second level are the four SWOT criteria that contribute to the overall goal. The criteria (sub-criteria) for strengths (S1, S2, S3), opportunities (O1, O2, O3), weaknesses (W1, W2, W3) and threats (T1, T2, T3) are individually presented into Level 2 and 3. On the second level is that four criteria are decomposed into twelve sub-criteria under SWOT; additionally on the bottom (or fourth) level, there are five alternative competitive strategies that are to be evaluated in terms of the sub-criteria listed on the third level. These competitive strategies (OS-1, OS-2, OW-1, TS-1 and TW-1) are assessed in Level 4 and illustrated in Fig.1.
A SWOT hierarchy for selecting the competitive strategies
First of all, the author invites the eighteen certificated ISO9000 or ISO14000 auditors (or lead auditors), consists of 14 part-time and 4 full-time auditors, to organize a TF in this particular study. They are first briefed about the overall objective of the study, then specifically on the SWOT and vote-ranking methodologies. The questionnaires are used for interviewing purposes; however they mainly use a board or group decision method to determine the criteria and sub-criteria for selecting the competitive strategies. The study is to apply the internal competitive strengths to find external market opportunities. This is followed by the analysis on the organization’s external competitive environment and internal operating environment. Consequently, the internal analysis is followed by the selection and implementation of strategies. Due to highly global nature of the “Green House”, the requirements of EMS are also applicable for other countries in the European Union. For selecting the competitive strategies of SMEs for EMS, the TF has been mainly on the discussion of the SWOT method and problem defining after a series of revision. The strategy combination of EMS for max {strengths, opportunities} and min {weaknesses, threats}, OS-1, OS-2, OW-1, TS-1 and TW-1, is provided in Table 1.
Strengths: +S1: Capability to execute and develop EMS certification +S2: Synergy with commerce, environmental protection and education units +S3: Possessing high level of environmental education | Weaknesses: -W1: SMEs respond slowly and difficultly for external customer requirements -W2: Some suppliers or manufacturers are unwilling to face higher environmental regulation required and seek other markets with lower quality consciousness -W3: The government’s regulations of environmental protection are too loose | |
Opportunities: +O1: Change in customers’ preferences (increase in market demand for EMS or QMS certification) +O2: Increase value-added of product +O3: Improvement in Green House and in environment | Maxi-maxi (O-S) Strategies OS-1: Extend EMS Certification effects to create high value-added markets OS-2: Involve in improving environment issues and promote company image and profits | Maxi-mini (O-W) Strategies OW-1:Change directly in manufacture preferences to create products of high environmental requirement standard |
Threats: -T1: Diminishing specific market demand and profitability -T2: Government or industry restrictive practices -T3: Negative corporation image if EMS certification is abandoned | Mini-maxi (T-S) Strategies TS-1: Increase strictly government or industry environmental regular | Mini-mini (T-W) Strategies TW-1: Government impel environmental education and assistance for SMEs |
SWOT and TOWS matrixes for EMS
The second task is to regard these SWOT indexes as candidates that are voted by TF. The four criteria are the strength, weakness, opportunity and threat indexes and the twelve sub-criteria are S1-3, W1-3, O1-3 and T1-3 alternatively, within the SWOT. They are regarded as the selected items and expected to receive votes with respect to the related elements within the model, as shown in Table 1.
The TF illustrate the order for the four criteria and the votes for each which are shown in Table 2. Every members will vote from 1 to S, (SR), where R is the number of criteria or sub-criteria. They are regarded as candidates whom are to be voted by different places. Afterward, TF will list its priority votes of sub-criteria in fixed first criterion within Table 3. They were only asked to determine the order of the criteria or sub-criteria, however not the weight of each criterion or sub-criterion.
Criteria | 1st | 2nd | 3rd | 4th | Weights |
Strengths | 4 | 8 | 4 | 2 | 0.264 |
Opportunities | 7 | 4 | 7 | 0 | 0.299 |
Weaknesses | 2 | 0 | 7 | 9 | 0.174 |
Threats | 5 | 6 | 0 | 7 | 0.263 |
Priority votes of four criteria.
The votes on Table 2 are used to calculate the weights of the four criteria by model (2),
Criteria | Votes | Weights | Votes | Weights | ||||||
1st | 2nd | 3rd | 1st | 2nd | 3rd | |||||
Strengths | Opportunities | |||||||||
S1 | 13 | 4 | 1 | 0.465 | O1 | 15 | 2 | 1 | 0.495 | |
S2 | 0 | 9 | 9 | 0.227 | O2 | 0 | 15 | 3 | 0.258 | |
S3 | 5 | 5 | 8 | 0.308 | O3 | 3 | 1 | 14 | 0.247 | |
Total | 18 | 18 | 18 | Total | 18 | 18 | 18 | |||
Weaknesses | Threats | |||||||||
W1 | 11 | 7 | 0 | 0.439 | T1 | 5 | 5 | 8 | 0.304 | |
W2 | 3 | 3 | 12 | 0.258 | T2 | 5 | 9 | 4 | 0.336 | |
W3 | 4 | 8 | 6 | 0.303 | T3 | 8 | 4 | 6 | 0.360 | |
Total | 18 | 18 | 18 | Total | 18 | 18 | 18 |
Priority votes and weights of twelve sub-criteria.
The competitive strategies, OS-1, OS-2, OW-1, TS-1 and TW-1 are subjective indices that could be translated into numerical ratings using different methods, such as questionnaire, AHP or vote-ranking and so much more. TF may ask their colleagues to answer these questionnaires in order to rate the competitive strategies of sub-criteria of each SWOT. A major problem was thus, to ensure the consistency between managers and to avoid any bias creeping in. A set of standard guidelines was placed after discussions with the TF (voters). It is mainly agreed that all performance scores would be based on a nine points grade scale. Each grade would have an adjective descriptor and an associated point score or range of point scores. The TF makes their judgment on the qualitative scale of adjectival descriptors.
Table 4 lists the example for rating the strength and opportunity indices, where the lower and upper scores are predetermined from 1-9. The strength and opportunity indices should be maximized, the least-favorable candidate is assigned the smallest value and the most-favorable candidate is assigned the largest value. On the other hand, the weakness and threat indices need to be minimized, where the least-favorable candidate is assigned the largest value and the most-favorable candidate is assigned the smallest value. The overcoming range of subjective indices is set between 1 and 9 illustrated in Table 5. Therefore, each of the competitive strategy can be awarded a ‘score’ from 1 to 9 on each sub-criterion.
Scores | Rules |
9 | Greatly conforming to market and sub-criteria of requirement, successful probability more than 90% |
7 | Better conforming to market and sub-criteria of requirement, successful probability about 70% |
5 | Conforming to market and sub-criteria of requirement, successful probability about 50% |
3 | Slightly conforming to market and sub-criteria of requirement, successful probability about 30% |
1 | Not conforming to market and sub-criteria of requirement, successful probability about 10% |
Grading different strategy scores in strength and opportunity indexes
Scores | Rules |
9 | Greatly overcoming sub-criteria requirement, successful probability more than 90% |
7 | Better overcoming sub-criteria requirement, successful probability about 70% |
5 | Exactly overcoming sub-criteria requirement, successful probability about 50% |
3 | Slightly overcoming sub-criteria requirement, successful probability about 30% |
1 | Not overcoming sub-criteria requirement, successful probability about 10% |
Grading different strategy scores in weakness and threat indexes
The five competitive strategies, OS-1, OS-2, OW-1, TS-1 and TW-1, by means of the highest rating were regarded as the best competitive strategies, with the rest being ranked accordingly. The competitive strategies will earn the average scores of questionnaires within Table 4 and Table 5 by TF. The average of collected scores is listed in the columns 5-9 of Table 6.
This step requires the TF to assess the performance of all the competitive strategies within the twelve sub-criteria of SWOT identified as important for competitive strategies rating. Simple score sheets were provided to assist the manager to record the scores for each strategy on each of the twelve sub-criteria. An example of this strategy is shown in Table 6. In the first row of Table 6, the number 0.123 is equal to the product of the “Strength” criterion score 0.264 multiply with the S1 given value of “0.465”. Moreover, the same method is applied to obtain other results. Once the weights for sub-criteria have been determined, it is relatively easy to calculate the resulting competitive strategies rating scores.
Mathematically, the rating is equivalent to the sum of the product of each sub-criterion weight and the competitive strategy performance score. The rating value of competitive strategies is obtained by summing the products of the respective elements. The competitive strategies rating value for strategy OS-1 is obtained by summing up the products of the respective elements in columns 4 and 5 for each row; given in the final column 10, the over all total weighted scores of the row is “6.859”. The rating method used in strategy OS-1, can be used to find the total scores of the other four strategies stated in columns 11-14 of Table 6.
The rating value for each competitive strategy is obtained by summing the products of the respective elements in the matrix; given in the final score, the values of over all competitive strategies of OS-1, OS-2, OW-1, TS-1 and TW-1 respectively is, 6.859, 8.357, 7.532, 7.298 and 8.274 stated within the last row of Table 6. This gave a rating score for each competitive strategy, whereas the higher the rating, the better the overall performance for competitive strategy.
In the last row of Table 6, the rating value for each strategy is obtained; the final score and the ranking of competitive strategies for OS-2, TW-1, OW-1, TS-1 and OS-1 is first, second, third, fourth and fifth respectively. Even though the score of OS-2 is only higher by 0.083 than TW-1 and the score of OW-1 is higher by 0.234 than TS-1, however for both of the competitive strategies, the difference of scores will definitely change the overall final rank. These results will be regarded as sensitivity analysis for five competitive strategies.
Criteria (A) | Sub-criteria (B) | Weights (C= A×B) | Grade Strategies Scores | Weighted Strategies Scores | ||||||||||
OS-1 | OS-2 | OW-1 | TS-1 | TW-1 | OS-1 | OS-2 | OW-1 | TS-1 | TW-1 | |||||
Strengths | S1 | 0.465 | 0.123 | 6.833 | 8.889 | 8.056 | 6.944 | 8.722 | 0.839 | 1.091 | 0.989 | 0.853 | 1.071 | |
0.264 | S2 | 0.227 | 0.060 | 6.944 | 8.944 | 7.611 | 7.278 | 8.500 | 0.416 | 0.536 | 0.456 | 0.436 | 0.509 | |
S3 | 0.308 | 0.081 | 7.056 | 8.833 | 7.556 | 8.611 | 8.611 | 0.574 | 0.718 | 0.614 | 0.700 | 0.700 | ||
Opportunities | O1 | 0.495 | 0.148 | 6.833 | 7.778 | 7.389 | 7.278 | 8.167 | 1.011 | 1.151 | 1.094 | 1.077 | 1.209 | |
0.299 | O2 | 0.258 | 0.077 | 6.944 | 8.000 | 7.611 | 7.389 | 8.000 | 0.536 | 0.617 | 0.587 | 0.570 | 0.617 | |
O3 | 0.247 | 0.074 | 6.778 | 8.111 | 7.778 | 7.500 | 7.944 | 0.501 | 0.599 | 0.574 | 0.554 | 0.587 | ||
Weaknesses | W1 | 0.439 | 0.076 | 6.611 | 7.778 | 7.222 | 6.944 | 7.833 | 0.505 | 0.594 | 0.552 | 0.530 | 0.598 | |
0.174 | W2 | 0.258 | 0.045 | 6.500 | 7.833 | 7.278 | 7.056 | 7.778 | 0.292 | 0.352 | 0.327 | 0.317 | 0.349 | |
W3 | 0.303 | 0.053 | 6.667 | 7.944 | 7.500 | 7.278 | 7.722 | 0.351 | 0.419 | 0.395 | 0.384 | 0.407 | ||
Threats | T1 | 0.304 | 0.080 | 7.056 | 8.611 | 7.278 | 7.000 | 8.444 | 0.564 | 0.688 | 0.582 | 0.560 | 0.675 | |
0.263 | T2 | 0.336 | 0.088 | 7.111 | 8.722 | 7.556 | 7.167 | 8.389 | 0.628 | 0.771 | 0.668 | 0.633 | 0.741 | |
T3 | 0.360 | 0.095 | 6.778 | 8.667 | 7.333 | 7.222 | 8.556 | 0.642 | 0.821 | 0.694 | 0.684 | 0.810 | ||
Total Weighted Scores | 6.859 | 8.357 | 7.532 | 7.298 | 8.274 |
The SWOT analysis of different strategies
First of all, considering that the strategy OS-1 has the lowest score within the strategy analysis, most of the SMEs supposed that this strategy is quite acceptable even though there are still have some doubts present, especially on whether or not by obtaining the EMS related international standard authentication, such as ISO14000, it will certainly create a high value-added market. From a present market condition which is quite unfeasible to reflect the practical demand, frequently as a final result it is invested in fund or modification. Even though most of the customers are quite optimistic and agreed to this way of doing, however when everything is fully involved within EMS in the future, it will certainly has some affect on its capital or product selling price. Which means that at the present moment, the demand on this particular product is lacking, furthermore, it might resulted in the incapability to agree on these certain analysis by some SMEs.
Moreover, from the strategy analysis OW-1and TS-1 point of view, direct changes in manufacture preferences to create products of high environmental requirement standard has a bigger risk toward the SMEs in term of direct investment. Generally, average companies do not have certain investment planning until it has reached a deal, order placement or customer’s promise in advance. Additionally, an increasingly strict government or industry environmental in carrying out this phase is facing difficulty, where presently the government mostly is using counseling method or fund assistance to encourage and urge the industry to increase its EMS ability in order to reach the low price product strategy and high level of product diversification.
Lastly, the strategy OS-2 imposes a similar way of thinking with strategy TW-1. Taiwanese SMEs apperceive the significance of EMS and also recognize the importance to survive within the diversified competing market environment, whereas they need to build up its environmental management that has to suit the EMS specification and attention. However, the investment within environmental protection for its resources and facilities requires a great amount of expenditures. Under this major investment, if the expected outcomes are unpredictable, therefore the willingness on investing within the environmental management will suffer an enormous drawback. These SMEs certainly would hope that government will work together with country resources, providing some assistance in procuring EMS needed facilities and equipments or even any related training within the environmental management scope, moreover guidance or counselling in obtaining different kinds of ISO authentic certificate will also be valuable resource.
Obviously, most people are familiar with the conflicts between environmental protection and economic development. Those who are convinced of the consequences of global warming will remain convinced, while those suspicious will remain suspicious. After all, economic development means bread, while the mankind cannot immediately appreciate the deep implications of its damage to the great nature. Therefore, politicians should be aware of the environmental implications of legal provisions and regulations. Likewise, the industry authorities, when developing new products, should consider the intangible social cost of pollution as a part of the overall cost and deal with the issue of pollution as a part of life cycle management, so that such considerations and practices will benefit our earth. In EMS, this will further our understanding of the potential poisonous substances to be produced in production, deployment and replacement stages, and will help us minimize pollution and thus contribute to environmental protection.
With the continuing development of human civilization and technology, the life cycle of any products, from production, consumption to final waste, it is involving more and more external adverse factors which bring about direct or indirect impact on the environment. Economists said that we should stop aggravation of global warming now; and there is only one earth; therefore, be environmentally friendly.
Lastly, the competitive strategies OS-2 and TW-1 will be provided to Taiwanese SME department and industry union. The main contributions of this study are as follows:
The selection procedure of competitive strategies in SWOT can assist the audience to think in a very comprehensive and detailed manner, while allowing them to categorize various issues.
In this field, many researchers have sought to improve the different capabilities of quantitative SWOT, such as AHP, ANP or fully rank decision-making units. In this case, the vote-ranking methodology incorporated with SWOT is applied and as a result, it became the easiest and most convenient method compared to others.
The vote-ranking is presented as an approach to the problem of ranking candidates in a preferential election. The future researches had suggested that the cross-evaluation method is better off to be applied to assess candidates through peer-group, whereas one can attain a more balanced view of the weight-setting. The cross evaluation can be used to overcome the problem of maverick decision-makers. The proposed methodology can be utilized to issues of SWOT, such as AHP or ANP within this study.
Biogas technology is a renewable energy technique from which biogas is obtained from biomass by anaerobic digestion of substrates obtained from Industrial, agricultural and municipal wastes [1]. It is has been acclaimed as an appropriate technology and has received global massive attention recently, and has equally been recommended as a strategy to ease global energy and environmental problems [2]. The potential of biomass as energy source have been estimated by different experts and scientists, using various assumptions and scenarios. For instance, the European Biomass Association (AEBIOM) asserted that the European production of biomass based energy can be increased from the 72 million tones in 2004 to 220 Mtoe in 2020 [3]. As the global trend is advocating for a transition from fossil energy waste to Renewable Energy (RE) based on several socio-economic and environmental justification, the necessity to embark on a process that ensures that biogas plants are properly sited for energy production is inevitable [4, 5, 6].
\nSiting of biogas plants in strategic locations is a major means of combating some of the environmental challenges of bio-waste generation; that would also be convenient and economically advantageous [7]. One of the biggest barriers in utilizing bio-waste in several countries is the dispersion of livestock farms across a given geographical location. This often leads to generation of relatively small or inadequate bio-waste; also most farms lack the technical capability of operating a farm scale biogas plant. Therefore, based on technical feasibility and economic viability, centralized large scale biogas production has been advocated, however suitable location for the plant requires geospatial consideration and location modeling. Implementation of spatial information technologies such as remote sensing and GIS in addressing this issue have been receiving enormous attention recently, and has been described as appropriate methodology to be utilized in site selection and analysis for biogas plant [8, 9]. The application of GIS as an appropriate tool for site suitability analysis by several researchers is a strong indicator of its capability to resolve location issues [10, 11, 12]. This study therefore attempted to present logical framework that would serve as a guide in the process of identifying suitable sites for biogas plant using the power of geospatial technology.
\nThe biogas plants for processing agricultural wastes are considered as those plants which are utilized in processing feedstock that have agricultural origin. Common or notable feedstock types for this kind of plants are vegetable residues and vegetable, animal manure and slurries, dedicated energy crops, sewage sludge, various residues from food industries etc. [1]. The design and technology of biogas plants differ from one country to another, it depends on the climatic conditions and national frameworks, energy or biomass availability and affordability. Based on sizes, functions and locations, agricultural AD plants can be classified as [3]:
Family scale biogas plants (very small scale)
Farm scale biogas plants (small or medium to large scale)
Centralized/ joint co-digestion plants (medium to large scale)
The benefits of biogas have increased the adoption rate amongst many countries. Biogas for instance can be utilized after treatment in numerous applications such as provision of electricity and heat generation, connection to the natural gas grid, or as biofuel in vehicles [13]. Several studies on biogas technology potentials and their adoption in various developing countries have shown that biogas technology has high potential in developing countries as an alternative energy source [15, 16, 17]. Further discussion on the benefits based on energy production, and environmental concern is discussed below:
\nAfter water, energy is a vital resource required for development globally, the demand is high especially in developing countries. To prevent further growth on the impact of climate change in most developing countries, it is argued that the energy market in such regions should be based on renewable sources [14]. Biogas as a renewable energy source, have received widespread adoption in Europe, they produce clean energy from organic waste and have framework for increase production [6]. The adoption of biogas technology based on energy interest in developing countries though faced with several barriers, critical information on viability of adoption of biogas technology is receiving attention currently by various researchers in these countries [7, 15, 16, 17].
\nBiogas technology is suitable for recycling various types of biomass waste, however the operational conditions and parameters are of interest since this can inhibit the microbial operations necessary for the digestion and may restrict the end use of digested biomass as a bio-fertilizer [18]. It is of major environmental interest that nutrient losses from residue of anaerobic digestion process is minimized when applied to the soil. Additionally, anaerobic treatment in a biogas plant reduces odor nuisances during slurry application, this is a welcome issue by many farmers [19]. Furthermore, biogas technology tend to reduce the potency of greenhouse gases (GHG), by capturing and combusting methane during anaerobic digestion and utilization as cook gas or energy source, GHG has been reported to be 21 times as much atmospheric warming potential as CO2 [20, 21]. Therefore, global attraction and adoption of biogas technology is hinged on the numerous environmental benefits associated with the technology.
\nGIS is a computer system that enhances capturing, checking, storing, integrating, analyzing, and displaying data about the earth in a spatially referenced way. The application of GIS is cross-disciplinary and has been adopted and applied in several fields of science and engineering. Land suitability analysis involves the search for the best location of one or more facilities to support some desired function, it involves the process to ascertain whether the land resource is appropriate for some desired uses and to determine its suitability level. Land suitability refers to the inherent suitability of the land for some specific, persistent uses. Examples range from retail site location to the location of multiple ambulance dispatch points. Land suitability analysis or assessment is achieved by considering certain land features such as hydrology, geography, topography, geology etc. in an enabled environment using GIS technologies [22].
\nGIS technology has been applied by various researchers in biogas plant location suitability assessment. Few instances are stated here: To develop potential for collective biogas plants in France, GIS was used to geo-reference the bio-resources potential and also to locate the optimal sites at both national and regional scales for the country [23], the final suitability map from the study provided constraint map and the energy potential grid, synthesized in the form of a raster GIS file. The study provided successfully a suitability map using precise geo-location of farms obtained through the analysis of aerial photographs and Landsat imagery used in the identification of crop residues. Similarly, a study was done to determine the optimal locations, sizes and number of biogas plants in Southern Finland, this study analyzed the spatial distribution and amount of potential biomass feedstock for bio-methane production for the study area [24]. In addition to numerous studies on site suitability analysis for biogas plant, a GIS based spatial data mining approach was adopted to model the optimal location for distributed biomass power generation facilities in Tumkur district, India [25], these studies asserted that GIS is an appropriate and recognized spatial tool for location analysis [23, 24, 25].
\nSuitable areas for biogas plants are evaluated to avoid close proximity to land features and uses that may be sensitive to the characteristics of utility-scale power production and waste streams. Sensitive land features to be avoided include surface water, wetlands, forests, public lands, highly sloped lands, and developed residential areas, with acceptable slopes of 14o or fewer [26]. Location and site suitability consideration for biogas plant includes:
\nAn application of GIS in determination of suitable sites is applied here using a typical biomass data collected in Anambra state of Nigeria for demonstration purposes.
\nThe study area for the application of GIS in biogas plant location analysis is Anambra State in South-east of Nigeria. The area is located between Latitudes 0504215611N and 0604513411N and Longitude 0603713011E and 0702513011E, it is surrounded by several states such as Delta State in the West, Imo State and Rivers State in the South, Enugu State in the East and Kogi State in the North. Data used in the study include primary and secondary data collected from various organizations, literatures and individuals. The flow chart of the research method is shown in Figure 1.
\nFlowchart of geospatial modeling for biogas plant.
\nFigure 1 present the research procedure which usually involves data collection and analysis. Data for site analysis of biogas plant was obtained from GIS organizations and agency, the biomass generation and sites data was obtained from field survey. The figure also shows the major layers used for the study. The primary data was collected from field survey through visit to slaughter houses in the study area, the biomass potentials was determined using Global Positioning System (GPS) receiver (Handheld GARMIN 76S), the biomass potential was based on paunch content generated in the various 43 abattoirs in the state. The data on the biomass generation capacity of all the abattoirs is shown in the appendix. GPS was used to obtain the geographical co-ordinate of the biomass source for geo-coding in data analysis. ArcGIS 10 software was used for all GIS operations and subsequent map production. The GIS-based thematic maps used for the production of the suitability map include political boundary map layer, Land Use and Land Cover (LULC) map obtained from the Landsat imagery, slope layer and the biomass layer. The land use map for the study area, was generated from the Landsat-7 ETM+ image and then classified to extract the different land uses of the study area using maximum likelihood classification algorithm. The residential and reserved areas in the study area were termed constrained areas and were exempted in the suitability map, the constrain map was made considering several environmental and socio-economical factors. The data types, format, scale and sources is shown in Table 1.
\nList of data | \nFormat/Map scale | \nSource | \n
---|---|---|
Land use map | \nLandsat-7 ETM + imagery | \nNational Remote Sensing Centre, Jos. | \n
Administrative layer map | \nArcinfo shapefile/digitalized from 1:50,000 scale map | \nSurvey deparment, Ministry of Lands, Survey and Town Planning, Awka | \n
Biomass source Location map | \nArcinfo shapefile | \nField trip to farms, Use of GPS | \n
DEM | \nSRTM imagery at 30 m resolution of 2000 | \n(http://www.landcover.org) | \n
List of data sources and format.
The site suitability was assessed using Eq. 1 below:
\nWhere Wi is the weighted score of the factor, Xi is the suitability rank of the factor, S is the suitability value for each factor and i is factor i.
\nData collection is critical in geospatial analysis, typical data used for this study include Land Use map classified from Remote sensing data source; geo-coded data of the biomass sources, this is usually in the form of point data, obtained using GPS device. The acquired data points and the value was transferred into Arcmap environment of ArcGIS and processed into vector map for the site suitability analysis. The result of the various data analysis and modeling of suitable sites for the biogas plant by excluding unwanted areas identified in the constrain map and overlaying the thematic maps is fully discussed below.
\nBased on prior knowledge of land use of some geographical co-ordinates points, six classes were categorized. They are agriculture areas, barren/open land, dense forest, sand, urban land, and water body. The classified land use map is shown in Figure 2.
\nLand cover and land use suitability map.
Assessment of classification accuracy was carried out using the scatter plot analysis in statistical toolbar in ArcGIS 10. All the training data were highlighted to compare the scatter plot of the six classes to each other. The classes were examined to detect any form of overlap (these are classes having different pixel value). This shown in Figure 3, the statistics for the training data was also used to assess the accuracy of the classification. The statistic are usually organized for each training area. The covariance statistics evaluates the correlation between the values of different bands and were adequate for the study.
\nScatter plot of image classification in ArcGIS.
The areas covered by each class of the LULC shows that urban land occupies 36.52% which represent 506896km2 of landmass of the overall LULC while the least class is the sand class followed by water body, these feature classes occupies landmass of 13080km2 and 14000km2 respectively. The overall classification accuracy determined is 83%. The Table of LULC classification of Anambra State, area occupied in km2 and percentage occupies by the various classes is shown in Table 4.1 (Table 2).
\nClass | \nArea(km) | \nPercentage (%) | \n
---|---|---|
WATER BODY | \n14000 | \n1.00 | \n
SAND | \n13080 | \n0.94 | \n
DENSE FOREST | \n257999 | \n18.59 | \n
URBAN LAND | \n506896 | \n36.52 | \n
AGRICULTURAL LAND | \n356430 | \n25.68 | \n
BARREN/OPEN LAND | \n239400 | \n17.25 | \n
TOTAL | \n1387805 | \n100 | \n
Area occupation of various LULC classes.
One of the basics for site analysis of biogas plant is the biomass potential density; Figure 4 shows the abattoir biomass data indicating areas where the bio-wastes are generated across the study area. Figure 4 shows the towns and villages in the State that has abattoir centers. From the Figure, there are no abattoir centres in the Northern parts of the state. There are highest number of abattoir biomass within the central region of the state, and scarce generating centers towards the extreme of the Southern parts of the state.
\nBiomass potential density of the study area.
Though the energy source from biomass in the study area could be utilized for biogas production and clean energy, this would be better to the current practices of burning wood in most homes and even in the abattoirs during meat processing operations. Biomass wastes generated during the slaughter of these animals includes blood, wastewater, ruminal content etc. The concentration of abattoirs in some of the areas in the study area is probably connected to the high population density of these areas. Since meat demand logically increases with increase in population. Areas with high concentration of biomass generation and clustering are best sites when considering proximity of waste sources as major criteria for bio-energy plant location. Figure 4 was used to produce a vector map of biomass spatial density layer, used in the final suitability analysis.
\nThe slope was derived from the digital elevation model of the study area. The slope of the study area was obtained through the slope function in spatial analyst tool in ArcGIS 10. The Slope command takes an input surface raster and calculates an output raster containing the slope at each cell, Figure 5 shows the slope of the study area.
\nSlope map of the study area.
The output slope raster is in percent (percent rise) as shown in the figure.
\nThe red color in the map represents areas in the study area that are less than 8%, thus they represent suitable locations to site biogas plant. Areas shown in the map within the range of 62–89% signifies high sloped areas, and should be avoided, this is of economic importance in siting and building of structures. Other land features such as hill-shade view for maximizing sunlight effect for increase in temperature for the biogas plant was created. Elevation layer was created from DEM to avoiding flood occurrence, all the layers was classified into 5 classes. The most suitable were sites with higher elevation, lower slope, and higher hill-view values. All the layers and land use layer obtained from Satellite imagery were overlaid using in ArcGIS using Weighted Overlay Tool. The result of such geospatial operation is the land use suitability map (not shown).
\nThe final suitability index map was obtained by overlaying the land use suitability map with the biomass spatial density layer. The output is shown in Figure 6 below. The suitable areas were divided into 4 classes- the Most Suitable, Highly Suitable, Moderate Suitable and Not Suitable. Figure 6 shows that the suitable sites are predominant in the East and central region of the study area. Extraction of these areas based on the selected criteria indicates the power of GIS in extracting useful geospatial information for geographical data analysis.
\nFinal suitability map. Source: [
This paper presents an application of GIS for site selection of bio-energy plant in Anambra State of Nigeria. Several environmental criteria and socio-economic factors were considered and used to obtain the land use suitability index map and biomass spatial density map. The final suitability index map was obtained by overlaying both maps. The suitable areas were divided into 4 classes- the Most Suitable, Highly Suitable, Moderate Suitable and Not Suitable and were predominant in the East and central region of the study area. This study therefore is a veritable framework tool in assessing and selecting suitable sites for siting of biogas treatment facilities.
\nLocations | \nAver. No. of cows Slaughtered daily | \nAmount Paunch (Kg/yr) | \n|
---|---|---|---|
1 | \nNkwo Igboukwu | \n5 | \n61137.5 | \n
2 | \nEke Ekwulobia | \n8.5 | \n103933.75 | \n
3 | \nOye Uga | \n10 | \n122275 | \n
4 | \nNwagu-Agulu | \n8 | \n97820 | \n
5 | \nAmikwo, Awka | \n18 | \n220095 | \n
6 | \nAmansea | \n23 | \n281232.5 | \n
7 | \nAfor-Igwe Umudioka | \n6.5 | \n79478.75 | \n
8 | \nUgwu-oye Ozubulu | \n8 | \n97820 | \n
9 | \nOraifite | \n3 | \n36682.5 | \n
10 | \nNkwo-Ogidi | \n14.5 | \n177298.75 | \n
11 | \nObosi | \n16 | \n195640 | \n
12 | \nNkpor Private | \n5 | \n61137.5 | \n
13 | \nNkpor | \n15 | \n183412.5 | \n
14 | \nAfor-Oba | \n7 | \n85592.5 | \n
15 | \nAfor-Nnobi | \n17 | \n207867.5 | \n
16 | \nEke-Awka Etiti | \n35 | \n427962.5 | \n
17 | \nEke-Agba, Uli | \n5 | \n61137.5 | \n
18 | \nAmorka | \n6 | \n73365 | \n
19 | \nNkwo Ogbe | \n10 | \n122275 | \n
20 | \nNkwo Okija | \n5 | \n61137.5 | \n
21 | \nIsseke | \n4 | \n48910 | \n
22 | \nOye-Agu Abagana | \n6 | \n73365 | \n
23 | \nEke-Agu | \n4 | \n48910 | \n
24 | \nNkwo-Nnewi | \n10.5 | \n128388.75 | \n
25 | \nOrie-Agbo | \n3 | \n36682.5 | \n
26 | \nOba-Isi Edo | \n14.5 | \n177298.75 | \n
27 | \nAmichi | \n4.5 | \n55023.75 | \n
28 | \nAfor-Ukpor | \n2 | \n24455 | \n
29 | \nOsumenyi Slaughter House | \n3 | \n36682.5 | \n
30 | \nUnubi Slaughter House | \n0.5 | \n6113.75 | \n
31 | \nIyi-owa Odekpe | \n2 | \n24455 | \n
32 | \nOchanja | \n70 | \n855925 | \n
33 | \nBridge-Head | \n11.5 | \n140616.25 | \n
34 | \nMarine | \n26 | \n317915 | \n
35 | \nUgwunabamkpa | \n1.5 | \n18341.25 | \n
36 | \nMain Mkt | \n20 | \n244550 | \n
37 | \nAfor Nanka | \n2 | \n24455 | \n
38 | \nEke Oko | \n8 | \n97820 | \n
39 | \nNkwo Umunze | \n4 | \n48910 | \n
40 | \nNteje | \n13 | \n158957.5 | \n
41 | \nOye-olisa Ogbunike | \n52.5 | \n641943.75 | \n
42 | \nUmunya | \n65 | \n794787.5 | \n
43 | \nOrie Awkuzu | \n12.5 | \n152843.75 | \n
\n |
Source: [28].
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