Abstract
1. INTRODUCTION Predicting the future of green brands is becoming increasingly necessary today as consumer awareness of environmental issues and demand for sustainable products increases. Understanding market trends and consumer behavior is critical for green brands to be competitive. Foresight enables green brands to innovate and develop new products that are in line with sustainability goals. One of the most important indicators for the development of green brands is the growth rate of their products in different areas. Given the key role of the term “green brand” and the need to be aware of its changes and developments, the question arises as to what pattern the development of green brands in different areas follows and what their growth trend will be in the future. Accordingly, the main objective of this research is to analyze the development trend of green brands and predict their future based on information from documents published in various fields of green brands.
2. MATERIALS AND METHODS
The purpose of this study is “applied”, i.e. Scientometrics. As far as the method of collecting the research data is concerned, it is a “descriptive survey”. The statistical population comprises all documents that have been published to date in various areas of green branding in scientometric studies. The necessary data extraction was carried out using information from the Scopus database. The statistical sample of the study also includes all scientific documents indexed in the Scopus citation database on green branding from 2000 to 2024. Accordingly, we categorized 14 topic areas into 5 green branding groups and then extracted the overall statistics of the number of publications in each category. The methods used include time series modeling with the Box-Jenkins method and goodness-of-fit tests with Akaike and Bayesian information criteria. The forecasting package of the statistical software R was used to perform these analyses.
The Akaike information criterion is a measure of goodness of fit that indicates how much information is missing in a statistical model. It is a comparative measure and does not in itself indicate whether a model fits well or poorly. It is defined as AIC -2log(L)+2K, where L is the likelihood function of the model and k is the number of model parameters considered. The model with the lowest AIC is considered to be a better fit than competing models for a data set. If the number of samples n is not very large, the corrected Akaike information criterion AICc is normally used as follows: AIC -2log(L)+2K(n/(n-k-1) )
3.RESULTS AND DISCUSSION
According to the Scopus publications database, economics, management and accounting have the highest share of the total number of publications at around 33 percent and computer science the lowest at around 11 percent. The time series graph of the number of publications in the five fields of economics, management and accounting, environmental sciences, social sciences, engineering and computer science shows an increasing trend. To test the appropriateness of each selected model, it must be ensured that the residuals resulting from the model fit are independent and follow a normal distribution with zero mean and constant variance. The mean prediction of the number of publications and its interval estimate at a 95% confidence level were estimated in each of the 5 areas. To create the possibility of prediction based on a mathematical model, we fit different ARIMA time series models to the desired data and finally select the best fitting model based on the AICc index. The best-fit model based on the AICc indices is the model with the lowest value of this index. Accordingly, the (1,2,0)ARIMA model was selected as the most appropriate time series model for analyzing the trend in the number of publications in the field of economics, management and accounting, the (0,2,1)ARIMA model was selected as the most appropriate time series model for analyzing the trend in the number of publications in the field of environmental sciences, the (2,2, 0)ARIMA model was selected as the most appropriate time series model for analyzing the trend in the number of publications in the field of social sciences, the (1,1,0)ARIMA model was selected as the most appropriate time series model for analyzing the trend in the number of publications in the field of engineering, and the (0,2,1)ARIMA model was selected as the most appropriate time series model for analyzing the trend in the number of publications in the field of computer sciences.
4.CONCLUSION
Based on the research results, a total of 3043 studies were conducted on the topic of “Green Branding” in 5 areas: Business, Management and Accounting, Environmental Science, Social Science, Engineering and Computer Science. Analyzing these publications in each field with the R software package led to the following conclusions as overall research results for the next 10 years based on a trend analysis.
As shown in the table1, social sciences will have the highest growth rate of 141.66% and engineering the lowest at 35.26% over the next ten years. The future trend of the 'green brand' keyword is critical as it reflects changing consumer preferences around sustainability, provides competitive advantage, supports compliance with evolving regulations and contributes to wider environmental goals. Companies that recognize and adapt to these trends are likely to thrive in an increasingly environmentally conscious market landscape. Since this study used the Scopus citation database to analyze the future of the term “green brand,” it is suggested that future researchers also use other databases such as Web of Science and extract the number of citations of publications and use them to analyze the trend.The future trend of the 'green brand' keyword is critical as it reflects changing consumer preferences around sustainability, provides competitive advantage, supports compliance with evolving regulations and contributes to wider environmental goals. Companies that recognize and adapt to these trends are likely to thrive in an increasingly environmentally conscious market landscape. Since this study used the Scopus citation database to analyze the future of the term “green brand,” it is suggested that future researchers also use other databases such as Web of Science and extract the number of citations of publications and use them to analyze the trend.



Table 1. Percentage growth in the number of publications over the next 10 years for each green brand area
Growth rate over the next 10 years Average total publications in 2034 Total number of publications in 2024 Green Brand Group Leader
74/58 625 358 Business, Management and Accounting
66/26 138 83 Environmental Sciences
141/66 261 108 Social Sciences
35/26 75 56 Engineering
85/48 115 62 Computer Science


Keywords: Trend analysis; Green brand categories; Scientometrics; Time series models; Growth rate forecasting