The effective implementation of sustainable forest management depends largely on carrying out forest operations in a sustainable manner. Climate change, as well as the increasing demand for forest products, requires a re-thinking of forest operations in terms of sustainability. In this context, it is important to understand the major driving factors for the future development of forest operations that promote economic, environmental and social well-being. The main objective of this paper is to identify important issues concerning forest operations and to propose a new paradigm towards sustainability in a changing climate, work and environmental conditions. Previously developed concepts of forest operations are reviewed, and a newly developed concept - Sustainable Forest Operations (SFO), is presented. Five key performance areas to ensure the sustainability of forest operations include: (i) environment; (ii) ergonomics; (iii) economics; (iv) quality optimization of products and production; and (v) people and society. Practical field examples are presented to demonstrate how these five interconnected principles are relevant to achieving sustainability, namely profit and wood quality maximization, ecological benefits, climate change mitigation, carbon sequestration, and forest workers' health and safety. The new concept of SFO provides integrated perspectives and approaches to effectively address ongoing and foreseeable challenges the global forest communities face, while balancing forest operations performance across economic, environmental and social sustainability. In this new concept, we emphasize the role of wood as a renewable and environmentally friendly material, and forest workers' safety and utilization efficiency and waste management as additional key elements of sustainability.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1016/j.scitotenv.2018.04.084 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
The role of the Mediterranean diet in reducing the risk of cognitive impairement, dementia, and Alzheimer's disease: a meta-analysis.
Geroscience
January 2025
Dept. of Bioinformatics, Semmelweis University, 1094, Budapest, Hungary.
Age-related cognitive impairment and dementia pose a significant global health, social, and economic challenge. While Alzheimer's disease (AD) has historically been viewed as the leading cause of dementia, recent evidence reveals the considerable impact of vascular cognitive impairment and dementia (VCID), which now accounts for nearly half of all dementia cases. The Mediterranean diet-characterized by high consumption of fruits, vegetables, whole grains, fish, and olive oil-has been widely recognized for its cardiovascular benefits and may also reduce the risk of cognitive decline and dementia.
View Article and Find Full Text PDFConstruction of a novel radioresistance-related signature for prediction of prognosis, immune microenvironment and anti-tumour drug sensitivity in non-small cell lung cancer.
Ann Med
December 2025
Department of Radio-Chemotherapy, Affiliated Hospital of Yangzhou University, Yangzhou University, Yangzhou, Jiangsu, China.
Background: Non-small cell lung cancer (NSCLC) is a fatal disease, and radioresistance is an important factor leading to treatment failure and disease progression. The objective of this research was to detect radioresistance-related genes (RRRGs) with prognostic value in NSCLC.
Methods: The weighted gene coexpression network analysis (WGCNA) and differentially expressed genes (DEGs) analysis were performed to identify RRRGs using expression profiles from TCGA and GEO databases.
Machine Learning-Based Prediction of Prostate Biopsy Necessity Using PSA, MRI, and Hematologic Parameters.
J Clin Med
December 2024
Department of Urology, Health Science University Eskisehir City Health Application and Research Center, 26080 Eskisehir, Turkey.
To establish a machine learning (ML) model for predicting prostate biopsy outcomes using prostate-specific antigen (PSA) values, multiparametric magnetic resonance imaging (mpMRI) findings, and hematologic parameters. The medical records of the patients who had undergone a prostate biopsy were evaluated. Laboratory findings, mpMRI findings, and prostate biopsy results were collected.
View Article and Find Full Text PDFEnhancing Manufacturing Precision: Leveraging Motor Currents Data of Computer Numerical Control Machines for Geometrical Accuracy Prediction Through Machine Learning.
Sensors (Basel)
December 2024
Intelligent Manufacturing Laboratory, Production Engineering Institute, Faculty of Mechanical Engineering, University of Maribor, Smetanova ulica 17, 2000 Maribor, Slovenia.
Direct verification of the geometric accuracy of machined parts cannot be performed simultaneously with active machining operations, as it usually requires subsequent inspection with measuring devices such as coordinate measuring machines (CMMs) or optical 3D scanners. This sequential approach increases production time and costs. In this study, we propose a novel indirect measurement method that utilizes motor current data from the controller of a Computer Numerical Control (CNC) machine in combination with machine learning algorithms to predict the geometric accuracy of machined parts in real-time.
View Article and Find Full Text PDFPredicting Postoperative Lung Cancer Recurrence and Survival Using Cox Proportional Hazards Regression and Machine Learning.
Cancers (Basel)
December 2024
Department of Radiology, School of Medicine, University of Pittsburgh, Pittsburgh, PA 15213, USA.
Background: Surgical resection remains the standard treatment for early-stage lung cancer. However, the recurrence rate after surgery is unacceptably high, ranging from 30% to 50%. Despite extensive efforts, accurately predicting the likelihood and timing of recurrence remains a significant challenge.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!