International Journal of Ecological Economics and Statistics™
The main aim of the International Journal of Ecological Economics and Statistics™ [ISSN 0973-1385 (Print); 0973-7537 (Online)] publishes refereed, well-written original research articles, and studies that describe the latest research and developments in ecological economics and ecological statistics. International Journal of Ecological Economics and Statistics™ (IJEES) is an interdisciplinary research journal in the area of environmental economics & environmental statistics. It's covers the many potential applications and connections to other areas of Science and technology such as the use and development of mathematical/ statistical modeling. IJEES is published four issues in a year. Reviewed, Abstracted and Indexed in the past: The IJEES is reviewed, abstracted and indexed in the past by the Thomson Reuters Web of Science [Database: Emerging Sources Citation Index], , EconLit, Journal of Economic Literature [JEL] (of American Economic Association); Current Index to Statistics (CIS) [ The Current Index to Statistics (CIS) is a joint venture of the American Statistical Association and Institute of Mathematical Statistics, USA]; ERA: Excellence in Research for Australia (by Govt. of Australia); IndexCopernicus; International Statistical Institute (ISI, Netherlands) Journal Index; Academic keys; Ulrich's Periodicals Directory; Indian Science Abstracts; Newjour; JournalSeek; Getcited; Zentralblatt MATH; and Mathface Index…
Regional AI Development and Household consumption in China: Confidence-Conditioned, Category-Level, and Heterogeneous Effects
China`s consumer market is enormous and evolving fast: in 2024, retail sales of consumer goods totaled ¥48.79 trillion, with online physical goods making up 26.8% of all retail purchases. At the same time, AI capacity has scaled rapidly—China accounted for about 61% of the world`s granted AI patents over 2010–2022, installed 276,288 industrial robots in 2023 alone, and drew roughly $9.3 billion in private AI investment in 2024. Yet the micro-behavioral channel from regional AI development to household spending remains under-examined, particularly across provinces with differing urbanization and fiscal capacity. Against this backdrop, this study uses data of 31 provinces of China over the period 2001-2022 and examines the impact of regional AI development on consumer spending behavior. Employing a two-way fixed effects regression method, the findings reveal that regional AI development positively influences consumer spending. A series of robustness tests also verifies these findings. The study also confirms the moderating effect of consumer confidence on this relationship. AI development significantly impacts consumption categories such as clothing, education, culture and entertainment, residence, healthcare, and household goods and services. The research highlights heterogeneous effects, showing that provinces with higher urbanization, industrialization, fiscal expenditures, and regional development benefit more from AI-driven consumer spending growth. These findings present numerous implications for consumers, policymakers and regional governments to adopt AI technologies, frame processes and policies to expand regional AI development across all areas, including less developed regions, to enhance consumer expenditure.
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Valuing the Invisible: Estimating the Preservation Value of Dibru Saikhowa National Park, Assam
Natural ecosystems provide vital services to human societies, many of which are intangible, invisible and excluded from market pricing. Dibru Saikhowa National Park (DSNP), a river-island national park in Assam, India and is one such ecologically rich but economically undervalued site. This study estimates the preservation value of DSNP using the Contingent Valuation Method (CVM). Based on a representative sample of 362 households, the study elicits individuals` willingness to pay (WTP) for continued preservation of DSNP, independent of any direct use. To ensure reliability, the survey incorporates rigorous bias-mitigation techniques addressing several biases and anomalies that affect CVM. Exploratory Factor Analysis (EFA) identifies two attitudinal dimensions viz., Warm-glow motive and Preservation motive, out of which only the latter significantly influences WTP. A Tobit regression model reveals that environmental awareness, education, and income positively affect WTP, while age and distance to the site show negative effects. To estimate the total preservation value for the state of Assam, the median monthly WTP was scaled up to all households and annualised. The resulting valuation, when compared with the annual government allocation for the area, reveals that public conservation spending amounts to only 0.013% of the preservation value, highlighting a stark underinvestment in conservation efforts. The findings highlight substantial public support for the non-use values of DSNP and underline the need to integrate such values into environmental budgeting and policy.
Robust Matrix Completion For Ecological Ordination: The RPCA-Eco Framework
This paper presents RPCA-Eco, a robust matrix completion framework designed for ecological ordination in the presence of substantial missing data. Building on the principles of Robust Principal Component Analysis (RPCA), RPCA-Eco integrates nuclear norm regularization with the Alternating Direction Method of Multipliers (ADMM) to recover low-rank ecological structures while isolating sparse anomalies. The method demonstrates high reconstruction fidelity and low bias even under extreme missingness levels (up to 70%), outperforming standard imputation techniques such as mean substitution, k-nearest neighbors, and MICE. Applied to macroinvertebrate data from the Okavango Delta, RPCA-Eco successfully recovers ecologically meaningful gradients despite incomplete and noisy observations. These results underscore its utility for biodiversity monitoring and conservation planning in data-sparse environments.
Ethnobotanical Insights into Medicinal Plants Used by the Inhabitants of Ilam City for Managing Musculoskeletal Pain, Western Iran
The use of traditional medicine and herbal remedies is deeply rooted in Iran, a country endowed with extensive biodiversity and a large variety of medicinal plant species. The present study aimed to document ethnobotanical knowledge related to medicinal plants traditionally employed for managing musculoskeletal pain (MSP) in Ilam, a city located in western Iran. A mixed-methods design incorporating both qualitative and quantitative components was adopted. Data were gathered through semi-structured interviews conducted with local herbalists and traditional healers. Plant species were identified using reputable sources, including online botanical databases, Dr. Valiollah Mozaffarian`s flora of Ilam, and expert support from the Medicinal Plants Center at Ilam University of Medical Sciences. In total, 30 medicinal plant species belonging to 23 families were recorded as remedies commonly used for MSP among the inhabitants of Ilam. The most frequently cited species included Achillea biebersteinii, Thymbra spicata L., Nigella sativa, Crocus haussknechtii, Datura innoxia Miller, Nerium oleander L., Ricinus communis, and Melissa officinalis. These taxa exhibited the highest levels of traditional relevance, with a usage report (UR) of 5, a relative frequency of citation (RFC) of 0.20, and a percentage familiarity and use (PFU) of 57.6%. Overall, the findings emphasize the significant role of native medicinal flora of Ilam in the traditional management of MSP. Beyond safeguarding valuable ethnobotanical heritage, these results also highlight promising prospects for developing novel phytotherapeutic agents. Further phytochemical and pharmacological investigations will be essential to validate the therapeutic efficacy of these plants and support their potential incorporation into contemporary medical practice
EV Battery Cost Competitiveness and Price Parity: Global Trends and Implications for India
Geopolitical strategies and insecurities over minerals like oil have highlighted the increasing dependence on resources for growth. This paper sheds light on how resources for electric vehicle (EV) batteries also affect international trade. We see, lithium-ion batteries'price is declining, while sodium-ion batteries are emerging, in both cases China leads globally. The year 2025 showcases an inflection point, which is an important landmark in the global EV transition as lithium-ion battery costs cross the long-awaited barrier for price difference with internal combustion engine (ICE) cars. The aspects of battery cost competitiveness in 2025 are explained in this paper: (i) technological drivers achieving breakthrough with the $100 (kWh) cost for lithium-ion battery (ii) existence of price differences between Western economies and China; (iii) inference for EV-ICE price concourse with in specific regards to India. The research signifies how systems' excess capacity in China and technological changes, especially the emergence of sodium-ion battery and Lithium Iron Phosphate (LFP) chemistry, are changing battery and vehicle freight economics at world level. It does this by utilizing secondary data from Bloomberg NEF, the International Energy Agency, and policy documents.