Purpose: This study aims to inventory and forecast Greenhouse Gas (GHG) emissions and formulate effective emission reduction strategies in the transportation and industrial sectors of Kendal Regency, with projections extending from 2024 to 2033. Methodology: This study employs Tier 1 and Tier 2 calculation methods based on the IPCC guidelines. Emission projections are modeled under a Business as Usual (BAU) scenario. Mitigation strategies were developed by assessing international best practices and adapting them to the local conditions. Result: By 2033, GHG emissions in the transportation sector are projected to reach 1,596,350 tons of CO? equivalent (CO?eq), while industrial sector emissions are estimated at 111,530.09 tons of CO?eq. The mitigation strategies proposed for the transportation sector could reduce emissions by up to 28%, whereas industrial sector strategies have the potential to cut emissions by up to 76%. Conclusions: Comprehensive mitigation strategies can significantly curb GHG emissions in Kendal Regency. The combination of technological advancements and policy-based interventions offers a robust framework for achieving substantial reductions across both sectors. Limitations: This study is limited by the availability and accuracy of local emission factor data and the assumptions used in BAU projections, which may not fully capture dynamic policy changes or technological breakthroughs. Contribution: This study provides a localized GHG emissions model for Kendal Regency, offering actionable, evidence-based strategies to policymakers and stakeholders to advance regional climate action goals.

Purpose: This study aims to develop a classification system for the Clerodendrum paniculatum plant (Bunga Pagoda), focusing on its key parts—stems, flowers, leaves, and trees—using the Convolutional Neural Network (CNN) algorithm. The objective is to support conservation efforts and facilitate digital data grouping through technology-based classification. Methodology: The research involved collecting a dataset of images representing different parts of the Clerodendrum paniculatum plant. These images were then used to train a CNN model. The training process included 200 epochs to optimize performance. The model's accuracy and performance were evaluated using a confusion matrix to measure classification success across the plant's various parts. Results: The CNN model achieved its highest accuracy of 97.78% when trained for 200 epochs. The results indicated a significant improvement in evaluation metrics compared to models trained with fewer epochs. The model successfully classified the plant parts with high precision, demonstrating its robustness and reliability for rare plant classification. Conclusions: This study confirms that the CNN algorithm is effective in classifying the parts of the Clerodendrum paniculatum plant. Increasing the number of training epochs substantially enhances the model's performance, making it a practical tool for digital plant conservation initiatives. Limitations: The study is limited by its reliance on a specific dataset, which may not encompass all possible variations of the Clerodendrum paniculatum plant under different environmental conditions. Contributions: This research contributes to digital plant conservation by developing a CNN-based classification system for rare plants. It highlights the importance of deep learning in biodiversity preservation and provides a foundation for future AI-driven botanical studies.